2023-03-03 15:21:27,460 - mmseg - INFO - Multi-processing start method is `None` 2023-03-03 15:21:27,474 - mmseg - INFO - OpenCV num_threads is `128 2023-03-03 15:21:27,474 - mmseg - INFO - OMP num threads is 1 2023-03-03 15:21:27,541 - mmseg - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.7.16 (default, Jan 17 2023, 22:20:44) [GCC 11.2.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/laizeqiang/miniconda3/envs/torch NVCC: Cuda compilation tools, release 11.6, V11.6.124 GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) PyTorch: 1.13.1 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.6 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.3.2 (built against CUDA 11.5) - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.14.1 OpenCV: 4.7.0 MMCV: 1.7.1 MMCV Compiler: GCC 9.3 MMCV CUDA Compiler: 11.6 MMSegmentation: 0.30.0+663c855 ------------------------------------------------------------ 2023-03-03 15:21:27,541 - mmseg - INFO - Distributed training: True 2023-03-03 15:21:28,252 - mmseg - INFO - Config: norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( type='EncoderDecoderFreeze', pretrained= 'pretrained/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth', backbone=dict( type='ResNetV1cCustomInitWeights', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), dilations=(1, 1, 2, 4), strides=(1, 2, 1, 1), norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, style='pytorch', contract_dilation=True), decode_head=dict( type='DepthwiseSeparableASPPHeadUnetFCHeadSingleStep', pretrained= 'pretrained/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth', dim=128, out_dim=256, unet_channels=528, dim_mults=[1, 1, 1], cat_embedding_dim=16, ignore_index=0, in_channels=2048, in_index=3, channels=512, dilations=(1, 12, 24, 36), c1_in_channels=256, c1_channels=48, dropout_ratio=0.1, num_classes=151, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), auxiliary_head=None, train_cfg=dict(), test_cfg=dict(mode='whole'), freeze_parameters=['backbone', 'decode_head']) dataset_type = 'ADE20K151Dataset' data_root = 'data/ade/ADEChallengeData2016' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) crop_size = (512, 512) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=False), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=0), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type='ADE20K151Dataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/training', ann_dir='annotations/training', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=False), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=0), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ]), val=dict( type='ADE20K151Dataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict( type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='ADE20K151Dataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict( type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) log_config = dict( interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] cudnn_benchmark = True optimizer = dict( type='AdamW', lr=0.00015, betas=[0.9, 0.96], weight_decay=0.045) optimizer_config = dict() lr_config = dict( policy='step', warmup='linear', warmup_iters=1000, warmup_ratio=1e-06, step=10000, gamma=0.5, min_lr=1e-06, by_epoch=False) runner = dict(type='IterBasedRunner', max_iters=80000) checkpoint_config = dict(by_epoch=False, interval=8000, max_keep_ckpts=1) evaluation = dict( interval=8000, metric='mIoU', pre_eval=True, save_best='mIoU') checkpoint = 'pretrained/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth' work_dir = './work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151' gpu_ids = range(0, 8) auto_resume = True 2023-03-03 15:21:32,886 - mmseg - INFO - Set random seed to 856706328, deterministic: False 2023-03-03 15:21:33,947 - mmseg - INFO - Parameters in backbone freezed! 2023-03-03 15:21:33,948 - mmseg - INFO - Trainable parameters in DepthwiseSeparableASPPHeadUnetFCHeadSingleStep: ['unet.init_conv.weight', 'unet.init_conv.bias', 'unet.time_mlp.1.weight', 'unet.time_mlp.1.bias', 'unet.time_mlp.3.weight', 'unet.time_mlp.3.bias', 'unet.downs.0.0.mlp.1.weight', 'unet.downs.0.0.mlp.1.bias', 'unet.downs.0.0.block1.proj.weight', 'unet.downs.0.0.block1.proj.bias', 'unet.downs.0.0.block1.norm.weight', 'unet.downs.0.0.block1.norm.bias', 'unet.downs.0.0.block2.proj.weight', 'unet.downs.0.0.block2.proj.bias', 'unet.downs.0.0.block2.norm.weight', 'unet.downs.0.0.block2.norm.bias', 'unet.downs.0.1.mlp.1.weight', 'unet.downs.0.1.mlp.1.bias', 'unet.downs.0.1.block1.proj.weight', 'unet.downs.0.1.block1.proj.bias', 'unet.downs.0.1.block1.norm.weight', 'unet.downs.0.1.block1.norm.bias', 'unet.downs.0.1.block2.proj.weight', 'unet.downs.0.1.block2.proj.bias', 'unet.downs.0.1.block2.norm.weight', 'unet.downs.0.1.block2.norm.bias', 'unet.downs.0.2.fn.fn.to_qkv.weight', 'unet.downs.0.2.fn.fn.to_out.0.weight', 'unet.downs.0.2.fn.fn.to_out.0.bias', 'unet.downs.0.2.fn.fn.to_out.1.g', 'unet.downs.0.2.fn.norm.g', 'unet.downs.0.3.weight', 'unet.downs.0.3.bias', 'unet.downs.1.0.mlp.1.weight', 'unet.downs.1.0.mlp.1.bias', 'unet.downs.1.0.block1.proj.weight', 'unet.downs.1.0.block1.proj.bias', 'unet.downs.1.0.block1.norm.weight', 'unet.downs.1.0.block1.norm.bias', 'unet.downs.1.0.block2.proj.weight', 'unet.downs.1.0.block2.proj.bias', 'unet.downs.1.0.block2.norm.weight', 'unet.downs.1.0.block2.norm.bias', 'unet.downs.1.1.mlp.1.weight', 'unet.downs.1.1.mlp.1.bias', 'unet.downs.1.1.block1.proj.weight', 'unet.downs.1.1.block1.proj.bias', 'unet.downs.1.1.block1.norm.weight', 'unet.downs.1.1.block1.norm.bias', 'unet.downs.1.1.block2.proj.weight', 'unet.downs.1.1.block2.proj.bias', 'unet.downs.1.1.block2.norm.weight', 'unet.downs.1.1.block2.norm.bias', 'unet.downs.1.2.fn.fn.to_qkv.weight', 'unet.downs.1.2.fn.fn.to_out.0.weight', 'unet.downs.1.2.fn.fn.to_out.0.bias', 'unet.downs.1.2.fn.fn.to_out.1.g', 'unet.downs.1.2.fn.norm.g', 'unet.downs.1.3.weight', 'unet.downs.1.3.bias', 'unet.downs.2.0.mlp.1.weight', 'unet.downs.2.0.mlp.1.bias', 'unet.downs.2.0.block1.proj.weight', 'unet.downs.2.0.block1.proj.bias', 'unet.downs.2.0.block1.norm.weight', 'unet.downs.2.0.block1.norm.bias', 'unet.downs.2.0.block2.proj.weight', 'unet.downs.2.0.block2.proj.bias', 'unet.downs.2.0.block2.norm.weight', 'unet.downs.2.0.block2.norm.bias', 'unet.downs.2.1.mlp.1.weight', 'unet.downs.2.1.mlp.1.bias', 'unet.downs.2.1.block1.proj.weight', 'unet.downs.2.1.block1.proj.bias', 'unet.downs.2.1.block1.norm.weight', 'unet.downs.2.1.block1.norm.bias', 'unet.downs.2.1.block2.proj.weight', 'unet.downs.2.1.block2.proj.bias', 'unet.downs.2.1.block2.norm.weight', 'unet.downs.2.1.block2.norm.bias', 'unet.downs.2.2.fn.fn.to_qkv.weight', 'unet.downs.2.2.fn.fn.to_out.0.weight', 'unet.downs.2.2.fn.fn.to_out.0.bias', 'unet.downs.2.2.fn.fn.to_out.1.g', 'unet.downs.2.2.fn.norm.g', 'unet.downs.2.3.weight', 'unet.downs.2.3.bias', 'unet.ups.0.0.mlp.1.weight', 'unet.ups.0.0.mlp.1.bias', 'unet.ups.0.0.block1.proj.weight', 'unet.ups.0.0.block1.proj.bias', 'unet.ups.0.0.block1.norm.weight', 'unet.ups.0.0.block1.norm.bias', 'unet.ups.0.0.block2.proj.weight', 'unet.ups.0.0.block2.proj.bias', 'unet.ups.0.0.block2.norm.weight', 'unet.ups.0.0.block2.norm.bias', 'unet.ups.0.0.res_conv.weight', 'unet.ups.0.0.res_conv.bias', 'unet.ups.0.1.mlp.1.weight', 'unet.ups.0.1.mlp.1.bias', 'unet.ups.0.1.block1.proj.weight', 'unet.ups.0.1.block1.proj.bias', 'unet.ups.0.1.block1.norm.weight', 'unet.ups.0.1.block1.norm.bias', 'unet.ups.0.1.block2.proj.weight', 'unet.ups.0.1.block2.proj.bias', 'unet.ups.0.1.block2.norm.weight', 'unet.ups.0.1.block2.norm.bias', 'unet.ups.0.1.res_conv.weight', 'unet.ups.0.1.res_conv.bias', 'unet.ups.0.2.fn.fn.to_qkv.weight', 'unet.ups.0.2.fn.fn.to_out.0.weight', 'unet.ups.0.2.fn.fn.to_out.0.bias', 'unet.ups.0.2.fn.fn.to_out.1.g', 'unet.ups.0.2.fn.norm.g', 'unet.ups.0.3.1.weight', 'unet.ups.0.3.1.bias', 'unet.ups.1.0.mlp.1.weight', 'unet.ups.1.0.mlp.1.bias', 'unet.ups.1.0.block1.proj.weight', 'unet.ups.1.0.block1.proj.bias', 'unet.ups.1.0.block1.norm.weight', 'unet.ups.1.0.block1.norm.bias', 'unet.ups.1.0.block2.proj.weight', 'unet.ups.1.0.block2.proj.bias', 'unet.ups.1.0.block2.norm.weight', 'unet.ups.1.0.block2.norm.bias', 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'unet.mid_block1.block2.norm.bias', 'unet.mid_attn.fn.fn.to_qkv.weight', 'unet.mid_attn.fn.fn.to_out.weight', 'unet.mid_attn.fn.fn.to_out.bias', 'unet.mid_attn.fn.norm.g', 'unet.mid_block2.mlp.1.weight', 'unet.mid_block2.mlp.1.bias', 'unet.mid_block2.block1.proj.weight', 'unet.mid_block2.block1.proj.bias', 'unet.mid_block2.block1.norm.weight', 'unet.mid_block2.block1.norm.bias', 'unet.mid_block2.block2.proj.weight', 'unet.mid_block2.block2.proj.bias', 'unet.mid_block2.block2.norm.weight', 'unet.mid_block2.block2.norm.bias', 'unet.final_res_block.mlp.1.weight', 'unet.final_res_block.mlp.1.bias', 'unet.final_res_block.block1.proj.weight', 'unet.final_res_block.block1.proj.bias', 'unet.final_res_block.block1.norm.weight', 'unet.final_res_block.block1.norm.bias', 'unet.final_res_block.block2.proj.weight', 'unet.final_res_block.block2.proj.bias', 'unet.final_res_block.block2.norm.weight', 'unet.final_res_block.block2.norm.bias', 'unet.final_res_block.res_conv.weight', 'unet.final_res_block.res_conv.bias', 'unet.final_conv.weight', 'unet.final_conv.bias', 'conv_seg_new.weight', 'conv_seg_new.bias'] 2023-03-03 15:21:33,948 - mmseg - INFO - Parameters in decode_head freezed! 2023-03-03 15:21:33,974 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth 2023-03-03 15:21:34,292 - mmseg - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: decode_head.conv_seg.weight, decode_head.conv_seg.bias, decode_head.image_pool.1.conv.weight, decode_head.image_pool.1.bn.weight, decode_head.image_pool.1.bn.bias, decode_head.image_pool.1.bn.running_mean, decode_head.image_pool.1.bn.running_var, decode_head.image_pool.1.bn.num_batches_tracked, decode_head.aspp_modules.0.conv.weight, decode_head.aspp_modules.0.bn.weight, decode_head.aspp_modules.0.bn.bias, decode_head.aspp_modules.0.bn.running_mean, decode_head.aspp_modules.0.bn.running_var, 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decode_head.aspp_modules.2.depthwise_conv.bn.running_var, decode_head.aspp_modules.2.depthwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.2.pointwise_conv.conv.weight, decode_head.aspp_modules.2.pointwise_conv.bn.weight, decode_head.aspp_modules.2.pointwise_conv.bn.bias, decode_head.aspp_modules.2.pointwise_conv.bn.running_mean, decode_head.aspp_modules.2.pointwise_conv.bn.running_var, decode_head.aspp_modules.2.pointwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.3.depthwise_conv.conv.weight, decode_head.aspp_modules.3.depthwise_conv.bn.weight, decode_head.aspp_modules.3.depthwise_conv.bn.bias, decode_head.aspp_modules.3.depthwise_conv.bn.running_mean, decode_head.aspp_modules.3.depthwise_conv.bn.running_var, decode_head.aspp_modules.3.depthwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.3.pointwise_conv.conv.weight, decode_head.aspp_modules.3.pointwise_conv.bn.weight, decode_head.aspp_modules.3.pointwise_conv.bn.bias, 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decode_head.sep_bottleneck.1.pointwise_conv.bn.running_mean, decode_head.sep_bottleneck.1.pointwise_conv.bn.running_var, decode_head.sep_bottleneck.1.pointwise_conv.bn.num_batches_tracked, auxiliary_head.conv_seg.weight, auxiliary_head.conv_seg.bias, auxiliary_head.convs.0.conv.weight, auxiliary_head.convs.0.bn.weight, auxiliary_head.convs.0.bn.bias, auxiliary_head.convs.0.bn.running_mean, auxiliary_head.convs.0.bn.running_var, auxiliary_head.convs.0.bn.num_batches_tracked 2023-03-03 15:21:34,308 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth 2023-03-03 15:21:34,593 - mmseg - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: backbone.stem.0.weight, backbone.stem.1.weight, backbone.stem.1.bias, backbone.stem.1.running_mean, backbone.stem.1.running_var, backbone.stem.1.num_batches_tracked, backbone.stem.3.weight, backbone.stem.4.weight, 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unet.mid_block2.block2.norm.bias, unet.final_res_block.mlp.1.weight, unet.final_res_block.mlp.1.bias, unet.final_res_block.block1.proj.weight, unet.final_res_block.block1.proj.bias, unet.final_res_block.block1.norm.weight, unet.final_res_block.block1.norm.bias, unet.final_res_block.block2.proj.weight, unet.final_res_block.block2.proj.bias, unet.final_res_block.block2.norm.weight, unet.final_res_block.block2.norm.bias, unet.final_res_block.res_conv.weight, unet.final_res_block.res_conv.bias, unet.final_conv.weight, unet.final_conv.bias, conv_seg_new.weight, conv_seg_new.bias, embed.weight 2023-03-03 15:21:34,626 - mmseg - INFO - EncoderDecoderFreeze( (backbone): ResNetV1cCustomInitWeights( (stem): Sequential( (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace=True) (6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (7): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace=True) ) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): ResLayer( (0): Bottleneck( (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer2): ResLayer( (0): Bottleneck( (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer3): ResLayer( (0): Bottleneck( (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (4): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (5): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer4): ResLayer( (0): Bottleneck( (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False) (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False) (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) ) init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth'} (decode_head): DepthwiseSeparableASPPHeadUnetFCHeadSingleStep( input_transform=None, ignore_index=0, align_corners=False (loss_decode): CrossEntropyLoss(avg_non_ignore=False) (conv_seg): None (dropout): Dropout2d(p=0.1, inplace=False) (image_pool): Sequential( (0): AdaptiveAvgPool2d(output_size=1) (1): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (aspp_modules): DepthwiseSeparableASPPModule( (0): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (1): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), groups=2048, bias=False) (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (2): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(24, 24), dilation=(24, 24), groups=2048, bias=False) (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (3): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(36, 36), dilation=(36, 36), groups=2048, bias=False) (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) ) (bottleneck): ConvModule( (conv): Conv2d(2560, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (c1_bottleneck): ConvModule( (conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (sep_bottleneck): Sequential( (0): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(560, 560, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=560, bias=False) (bn): SyncBatchNorm(560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(560, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (1): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) ) (unet): Unet( (init_conv): Conv2d(528, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) (time_mlp): Sequential( (0): SinusoidalPosEmb() (1): Linear(in_features=128, out_features=512, bias=True) (2): GELU(approximate='none') (3): Linear(in_features=512, out_features=512, bias=True) ) (downs): ModuleList( (0): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (1): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (2): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (ups): ModuleList( (0): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Sequential( (0): Upsample(scale_factor=2.0, mode=nearest) (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (1): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Sequential( (0): Upsample(scale_factor=2.0, mode=nearest) (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (2): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (mid_block1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (mid_attn): Residual( (fn): PreNorm( (fn): Attention( (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) ) (norm): LayerNorm() ) ) (mid_block2): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (final_res_block): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=512, out_features=256, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 128, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) ) (final_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) ) (conv_seg_new): Conv2d(256, 151, kernel_size=(1, 1), stride=(1, 1)) (embed): Embedding(151, 16) ) init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth'} ) 2023-03-03 15:21:35,459 - mmseg - INFO - Loaded 20210 images 2023-03-03 15:21:36,671 - mmseg - INFO - Loaded 2000 images 2023-03-03 15:21:36,674 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-153, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151 2023-03-03 15:21:36,674 - mmseg - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook -------------------- after_train_iter: (ABOVE_NORMAL) OptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (LOW ) IterTimerHook (VERY_LOW ) TextLoggerHook -------------------- before_val_iter: (LOW ) IterTimerHook -------------------- after_val_iter: (LOW ) IterTimerHook -------------------- after_val_epoch: (VERY_LOW ) TextLoggerHook -------------------- after_run: (VERY_LOW ) TextLoggerHook -------------------- 2023-03-03 15:21:36,674 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters 2023-03-03 15:21:36,674 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151 by HardDiskBackend. 2023-03-03 15:22:25,790 - mmseg - INFO - Iter [50/80000] lr: 7.350e-06, eta: 10:15:27, time: 0.462, data_time: 0.012, memory: 37833, decode.loss_ce: 3.7584, decode.acc_seg: 17.5461, loss: 3.7584 2023-03-03 15:22:35,992 - mmseg - INFO - Iter [100/80000] lr: 1.485e-05, eta: 7:23:22, time: 0.204, data_time: 0.006, memory: 37833, decode.loss_ce: 2.8421, decode.acc_seg: 50.9735, loss: 2.8421 2023-03-03 15:22:46,306 - mmseg - INFO - Iter [150/80000] lr: 2.235e-05, eta: 6:26:54, time: 0.206, data_time: 0.006, memory: 37833, decode.loss_ce: 1.9973, decode.acc_seg: 59.9421, loss: 1.9973 2023-03-03 15:22:56,274 - mmseg - INFO - Iter [200/80000] lr: 2.985e-05, eta: 5:56:17, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 1.3628, decode.acc_seg: 68.5600, loss: 1.3628 2023-03-03 15:23:06,166 - mmseg - INFO - Iter [250/80000] lr: 3.735e-05, eta: 5:37:26, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 1.0209, decode.acc_seg: 75.7475, loss: 1.0209 2023-03-03 15:23:16,211 - mmseg - INFO - Iter [300/80000] lr: 4.485e-05, eta: 5:25:30, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.7799, decode.acc_seg: 79.9233, loss: 0.7799 2023-03-03 15:23:26,261 - mmseg - INFO - Iter [350/80000] lr: 5.235e-05, eta: 5:16:55, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.6068, decode.acc_seg: 83.8561, loss: 0.6068 2023-03-03 15:23:36,267 - mmseg - INFO - Iter [400/80000] lr: 5.985e-05, eta: 5:10:20, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.4887, decode.acc_seg: 86.0661, loss: 0.4887 2023-03-03 15:23:46,332 - mmseg - INFO - Iter [450/80000] lr: 6.735e-05, eta: 5:05:20, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.4140, decode.acc_seg: 87.0521, loss: 0.4140 2023-03-03 15:23:56,486 - mmseg - INFO - Iter [500/80000] lr: 7.485e-05, eta: 5:01:31, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.3805, decode.acc_seg: 87.8813, loss: 0.3805 2023-03-03 15:24:06,477 - mmseg - INFO - Iter [550/80000] lr: 8.235e-05, eta: 4:58:00, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.3613, decode.acc_seg: 87.9509, loss: 0.3613 2023-03-03 15:24:16,742 - mmseg - INFO - Iter [600/80000] lr: 8.985e-05, eta: 4:55:38, time: 0.205, data_time: 0.006, memory: 37833, decode.loss_ce: 0.3377, decode.acc_seg: 88.4160, loss: 0.3377 2023-03-03 15:24:29,519 - mmseg - INFO - Iter [650/80000] lr: 9.735e-05, eta: 4:58:43, time: 0.256, data_time: 0.053, memory: 37833, decode.loss_ce: 0.3147, decode.acc_seg: 88.8365, loss: 0.3147 2023-03-03 15:24:39,493 - mmseg - INFO - Iter [700/80000] lr: 1.049e-04, eta: 4:56:02, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.3126, decode.acc_seg: 88.9790, loss: 0.3126 2023-03-03 15:24:49,524 - mmseg - INFO - Iter [750/80000] lr: 1.124e-04, eta: 4:53:47, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.3076, decode.acc_seg: 88.7991, loss: 0.3076 2023-03-03 15:24:59,764 - mmseg - INFO - Iter [800/80000] lr: 1.199e-04, eta: 4:52:09, time: 0.205, data_time: 0.006, memory: 37833, decode.loss_ce: 0.3039, decode.acc_seg: 88.9406, loss: 0.3039 2023-03-03 15:25:09,744 - mmseg - INFO - Iter [850/80000] lr: 1.274e-04, eta: 4:50:17, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.3148, decode.acc_seg: 88.6242, loss: 0.3148 2023-03-03 15:25:19,710 - mmseg - INFO - Iter [900/80000] lr: 1.349e-04, eta: 4:48:34, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2946, decode.acc_seg: 89.1246, loss: 0.2946 2023-03-03 15:25:29,774 - mmseg - INFO - Iter [950/80000] lr: 1.424e-04, eta: 4:47:10, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2859, decode.acc_seg: 89.3700, loss: 0.2859 2023-03-03 15:25:39,777 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 15:25:39,777 - mmseg - INFO - Iter [1000/80000] lr: 1.499e-04, eta: 4:45:48, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2950, decode.acc_seg: 89.0203, loss: 0.2950 2023-03-03 15:25:49,830 - mmseg - INFO - Iter [1050/80000] lr: 1.500e-04, eta: 4:44:37, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2917, decode.acc_seg: 89.0381, loss: 0.2917 2023-03-03 15:25:59,987 - mmseg - INFO - Iter [1100/80000] lr: 1.500e-04, eta: 4:43:39, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2821, decode.acc_seg: 89.5100, loss: 0.2821 2023-03-03 15:26:10,099 - mmseg - INFO - Iter [1150/80000] lr: 1.500e-04, eta: 4:42:42, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2933, decode.acc_seg: 88.8762, loss: 0.2933 2023-03-03 15:26:20,168 - mmseg - INFO - Iter [1200/80000] lr: 1.500e-04, eta: 4:41:46, time: 0.201, data_time: 0.007, memory: 37833, decode.loss_ce: 0.2789, decode.acc_seg: 89.6711, loss: 0.2789 2023-03-03 15:26:30,240 - mmseg - INFO - Iter [1250/80000] lr: 1.500e-04, eta: 4:40:54, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2743, decode.acc_seg: 89.4279, loss: 0.2743 2023-03-03 15:26:42,637 - mmseg - INFO - Iter [1300/80000] lr: 1.500e-04, eta: 4:42:26, time: 0.248, data_time: 0.053, memory: 37833, decode.loss_ce: 0.2804, decode.acc_seg: 89.2859, loss: 0.2804 2023-03-03 15:26:52,759 - mmseg - INFO - Iter [1350/80000] lr: 1.500e-04, eta: 4:41:38, time: 0.203, data_time: 0.007, memory: 37833, decode.loss_ce: 0.2826, decode.acc_seg: 89.2886, loss: 0.2826 2023-03-03 15:27:02,683 - mmseg - INFO - Iter [1400/80000] lr: 1.500e-04, eta: 4:40:41, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2763, decode.acc_seg: 89.6015, loss: 0.2763 2023-03-03 15:27:12,733 - mmseg - INFO - Iter [1450/80000] lr: 1.500e-04, eta: 4:39:55, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2717, decode.acc_seg: 89.5416, loss: 0.2717 2023-03-03 15:27:22,737 - mmseg - INFO - Iter [1500/80000] lr: 1.500e-04, eta: 4:39:08, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2679, decode.acc_seg: 89.8716, loss: 0.2679 2023-03-03 15:27:32,871 - mmseg - INFO - Iter [1550/80000] lr: 1.500e-04, eta: 4:38:30, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2577, decode.acc_seg: 90.0116, loss: 0.2577 2023-03-03 15:27:43,022 - mmseg - INFO - Iter [1600/80000] lr: 1.500e-04, eta: 4:37:55, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2658, decode.acc_seg: 89.9595, loss: 0.2658 2023-03-03 15:27:53,101 - mmseg - INFO - Iter [1650/80000] lr: 1.500e-04, eta: 4:37:18, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2765, decode.acc_seg: 89.5506, loss: 0.2765 2023-03-03 15:28:03,198 - mmseg - INFO - Iter [1700/80000] lr: 1.500e-04, eta: 4:36:43, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2645, decode.acc_seg: 90.0193, loss: 0.2645 2023-03-03 15:28:13,096 - mmseg - INFO - Iter [1750/80000] lr: 1.500e-04, eta: 4:36:01, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2672, decode.acc_seg: 89.8001, loss: 0.2672 2023-03-03 15:28:23,080 - mmseg - INFO - Iter [1800/80000] lr: 1.500e-04, eta: 4:35:25, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2647, decode.acc_seg: 90.0645, loss: 0.2647 2023-03-03 15:28:33,139 - mmseg - INFO - Iter [1850/80000] lr: 1.500e-04, eta: 4:34:53, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2594, decode.acc_seg: 90.1447, loss: 0.2594 2023-03-03 15:28:45,713 - mmseg - INFO - Iter [1900/80000] lr: 1.500e-04, eta: 4:36:05, time: 0.251, data_time: 0.054, memory: 37833, decode.loss_ce: 0.2818, decode.acc_seg: 89.3505, loss: 0.2818 2023-03-03 15:28:55,806 - mmseg - INFO - Iter [1950/80000] lr: 1.500e-04, eta: 4:35:34, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2589, decode.acc_seg: 90.1635, loss: 0.2589 2023-03-03 15:29:05,702 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 15:29:05,702 - mmseg - INFO - Iter [2000/80000] lr: 1.500e-04, eta: 4:34:56, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2561, decode.acc_seg: 90.2004, loss: 0.2561 2023-03-03 15:29:15,709 - mmseg - INFO - Iter [2050/80000] lr: 1.500e-04, eta: 4:34:24, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2574, decode.acc_seg: 90.1083, loss: 0.2574 2023-03-03 15:29:25,667 - mmseg - INFO - Iter [2100/80000] lr: 1.500e-04, eta: 4:33:51, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2662, decode.acc_seg: 89.9077, loss: 0.2662 2023-03-03 15:29:35,673 - mmseg - INFO - Iter [2150/80000] lr: 1.500e-04, eta: 4:33:21, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2664, decode.acc_seg: 89.7308, loss: 0.2664 2023-03-03 15:29:45,686 - mmseg - INFO - Iter [2200/80000] lr: 1.500e-04, eta: 4:32:52, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2574, decode.acc_seg: 90.2534, loss: 0.2574 2023-03-03 15:29:55,646 - mmseg - INFO - Iter [2250/80000] lr: 1.500e-04, eta: 4:32:22, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2555, decode.acc_seg: 90.2183, loss: 0.2555 2023-03-03 15:30:05,745 - mmseg - INFO - Iter [2300/80000] lr: 1.500e-04, eta: 4:31:58, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2622, decode.acc_seg: 90.1114, loss: 0.2622 2023-03-03 15:30:15,988 - mmseg - INFO - Iter [2350/80000] lr: 1.500e-04, eta: 4:31:39, time: 0.205, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2628, decode.acc_seg: 89.7957, loss: 0.2628 2023-03-03 15:30:25,999 - mmseg - INFO - Iter [2400/80000] lr: 1.500e-04, eta: 4:31:13, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2520, decode.acc_seg: 90.2810, loss: 0.2520 2023-03-03 15:30:36,028 - mmseg - INFO - Iter [2450/80000] lr: 1.500e-04, eta: 4:30:48, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2685, decode.acc_seg: 89.8409, loss: 0.2685 2023-03-03 15:30:46,021 - mmseg - INFO - Iter [2500/80000] lr: 1.500e-04, eta: 4:30:22, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2624, decode.acc_seg: 89.9938, loss: 0.2624 2023-03-03 15:30:58,436 - mmseg - INFO - Iter [2550/80000] lr: 1.500e-04, eta: 4:31:11, time: 0.248, data_time: 0.054, memory: 37833, decode.loss_ce: 0.2747, decode.acc_seg: 89.6004, loss: 0.2747 2023-03-03 15:31:08,337 - mmseg - INFO - Iter [2600/80000] lr: 1.500e-04, eta: 4:30:42, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2613, decode.acc_seg: 90.0960, loss: 0.2613 2023-03-03 15:31:18,483 - mmseg - INFO - Iter [2650/80000] lr: 1.500e-04, eta: 4:30:22, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2603, decode.acc_seg: 90.0241, loss: 0.2603 2023-03-03 15:31:28,560 - mmseg - INFO - Iter [2700/80000] lr: 1.500e-04, eta: 4:30:00, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2594, decode.acc_seg: 89.9317, loss: 0.2594 2023-03-03 15:31:38,478 - mmseg - INFO - Iter [2750/80000] lr: 1.500e-04, eta: 4:29:33, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2558, decode.acc_seg: 90.1229, loss: 0.2558 2023-03-03 15:31:48,437 - mmseg - INFO - Iter [2800/80000] lr: 1.500e-04, eta: 4:29:09, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2583, decode.acc_seg: 90.0247, loss: 0.2583 2023-03-03 15:31:58,567 - mmseg - INFO - Iter [2850/80000] lr: 1.500e-04, eta: 4:28:49, time: 0.203, data_time: 0.007, memory: 37833, decode.loss_ce: 0.2548, decode.acc_seg: 90.2297, loss: 0.2548 2023-03-03 15:32:08,487 - mmseg - INFO - Iter [2900/80000] lr: 1.500e-04, eta: 4:28:25, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2473, decode.acc_seg: 90.4099, loss: 0.2473 2023-03-03 15:32:18,447 - mmseg - INFO - Iter [2950/80000] lr: 1.500e-04, eta: 4:28:02, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2605, decode.acc_seg: 89.8392, loss: 0.2605 2023-03-03 15:32:28,404 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 15:32:28,404 - mmseg - INFO - Iter [3000/80000] lr: 1.500e-04, eta: 4:27:39, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2492, decode.acc_seg: 90.3541, loss: 0.2492 2023-03-03 15:32:38,521 - mmseg - INFO - Iter [3050/80000] lr: 1.500e-04, eta: 4:27:21, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2667, decode.acc_seg: 89.8612, loss: 0.2667 2023-03-03 15:32:48,546 - mmseg - INFO - Iter [3100/80000] lr: 1.500e-04, eta: 4:27:00, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2471, decode.acc_seg: 90.3464, loss: 0.2471 2023-03-03 15:32:58,629 - mmseg - INFO - Iter [3150/80000] lr: 1.500e-04, eta: 4:26:42, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2462, decode.acc_seg: 90.2952, loss: 0.2462 2023-03-03 15:33:11,326 - mmseg - INFO - Iter [3200/80000] lr: 1.500e-04, eta: 4:27:26, time: 0.254, data_time: 0.055, memory: 37833, decode.loss_ce: 0.2658, decode.acc_seg: 89.9575, loss: 0.2658 2023-03-03 15:33:21,285 - mmseg - INFO - Iter [3250/80000] lr: 1.500e-04, eta: 4:27:04, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2469, decode.acc_seg: 90.3870, loss: 0.2469 2023-03-03 15:33:31,432 - mmseg - INFO - Iter [3300/80000] lr: 1.500e-04, eta: 4:26:47, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2582, decode.acc_seg: 90.0501, loss: 0.2582 2023-03-03 15:33:41,737 - mmseg - INFO - Iter [3350/80000] lr: 1.500e-04, eta: 4:26:34, time: 0.206, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2474, decode.acc_seg: 90.5089, loss: 0.2474 2023-03-03 15:33:51,669 - mmseg - INFO - Iter [3400/80000] lr: 1.500e-04, eta: 4:26:12, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2435, decode.acc_seg: 90.3625, loss: 0.2435 2023-03-03 15:34:01,610 - mmseg - INFO - Iter [3450/80000] lr: 1.500e-04, eta: 4:25:50, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2542, decode.acc_seg: 90.2660, loss: 0.2542 2023-03-03 15:34:11,582 - mmseg - INFO - Iter [3500/80000] lr: 1.500e-04, eta: 4:25:30, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2520, decode.acc_seg: 90.4583, loss: 0.2520 2023-03-03 15:34:21,528 - mmseg - INFO - Iter [3550/80000] lr: 1.500e-04, eta: 4:25:10, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2452, decode.acc_seg: 90.6267, loss: 0.2452 2023-03-03 15:34:31,406 - mmseg - INFO - Iter [3600/80000] lr: 1.500e-04, eta: 4:24:48, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2574, decode.acc_seg: 90.0655, loss: 0.2574 2023-03-03 15:34:41,428 - mmseg - INFO - Iter [3650/80000] lr: 1.500e-04, eta: 4:24:30, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2454, decode.acc_seg: 90.3807, loss: 0.2454 2023-03-03 15:34:51,354 - mmseg - INFO - Iter [3700/80000] lr: 1.500e-04, eta: 4:24:10, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2462, decode.acc_seg: 90.3769, loss: 0.2462 2023-03-03 15:35:01,570 - mmseg - INFO - Iter [3750/80000] lr: 1.500e-04, eta: 4:23:56, time: 0.204, data_time: 0.007, memory: 37833, decode.loss_ce: 0.2524, decode.acc_seg: 90.1939, loss: 0.2524 2023-03-03 15:35:14,112 - mmseg - INFO - Iter [3800/80000] lr: 1.500e-04, eta: 4:24:29, time: 0.251, data_time: 0.053, memory: 37833, decode.loss_ce: 0.2663, decode.acc_seg: 89.6288, loss: 0.2663 2023-03-03 15:35:24,122 - mmseg - INFO - Iter [3850/80000] lr: 1.500e-04, eta: 4:24:11, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2549, decode.acc_seg: 90.0363, loss: 0.2549 2023-03-03 15:35:34,136 - mmseg - INFO - Iter [3900/80000] lr: 1.500e-04, eta: 4:23:53, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2568, decode.acc_seg: 90.1424, loss: 0.2568 2023-03-03 15:35:44,085 - mmseg - INFO - Iter [3950/80000] lr: 1.500e-04, eta: 4:23:33, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2480, decode.acc_seg: 90.3698, loss: 0.2480 2023-03-03 15:35:53,974 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 15:35:53,974 - mmseg - INFO - Iter [4000/80000] lr: 1.500e-04, eta: 4:23:13, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2531, decode.acc_seg: 90.1218, loss: 0.2531 2023-03-03 15:36:04,013 - mmseg - INFO - Iter [4050/80000] lr: 1.500e-04, eta: 4:22:56, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2440, decode.acc_seg: 90.6974, loss: 0.2440 2023-03-03 15:36:14,032 - mmseg - INFO - Iter [4100/80000] lr: 1.500e-04, eta: 4:22:39, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2420, decode.acc_seg: 90.5301, loss: 0.2420 2023-03-03 15:36:23,942 - mmseg - INFO - Iter [4150/80000] lr: 1.500e-04, eta: 4:22:20, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2520, decode.acc_seg: 90.1645, loss: 0.2520 2023-03-03 15:36:33,999 - mmseg - INFO - Iter [4200/80000] lr: 1.500e-04, eta: 4:22:04, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2470, decode.acc_seg: 90.4026, loss: 0.2470 2023-03-03 15:36:43,973 - mmseg - INFO - Iter [4250/80000] lr: 1.500e-04, eta: 4:21:47, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2432, decode.acc_seg: 90.5615, loss: 0.2432 2023-03-03 15:36:53,856 - mmseg - INFO - Iter [4300/80000] lr: 1.500e-04, eta: 4:21:28, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2392, decode.acc_seg: 90.7964, loss: 0.2392 2023-03-03 15:37:03,878 - mmseg - INFO - Iter [4350/80000] lr: 1.500e-04, eta: 4:21:11, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2512, decode.acc_seg: 90.0686, loss: 0.2512 2023-03-03 15:37:13,836 - mmseg - INFO - Iter [4400/80000] lr: 1.500e-04, eta: 4:20:54, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2552, decode.acc_seg: 90.0494, loss: 0.2552 2023-03-03 15:37:26,458 - mmseg - INFO - Iter [4450/80000] lr: 1.500e-04, eta: 4:21:22, time: 0.252, data_time: 0.052, memory: 37833, decode.loss_ce: 0.2414, decode.acc_seg: 90.4526, loss: 0.2414 2023-03-03 15:37:36,591 - mmseg - INFO - Iter [4500/80000] lr: 1.500e-04, eta: 4:21:08, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2454, decode.acc_seg: 90.2956, loss: 0.2454 2023-03-03 15:37:46,493 - mmseg - INFO - Iter [4550/80000] lr: 1.500e-04, eta: 4:20:50, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2480, decode.acc_seg: 90.3783, loss: 0.2480 2023-03-03 15:37:56,425 - mmseg - INFO - Iter [4600/80000] lr: 1.500e-04, eta: 4:20:32, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2565, decode.acc_seg: 90.1710, loss: 0.2565 2023-03-03 15:38:06,482 - mmseg - INFO - Iter [4650/80000] lr: 1.500e-04, eta: 4:20:17, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2473, decode.acc_seg: 90.3880, loss: 0.2473 2023-03-03 15:38:16,445 - mmseg - INFO - Iter [4700/80000] lr: 1.500e-04, eta: 4:20:00, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2426, decode.acc_seg: 90.4386, loss: 0.2426 2023-03-03 15:38:26,456 - mmseg - INFO - Iter [4750/80000] lr: 1.500e-04, eta: 4:19:44, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2489, decode.acc_seg: 90.2607, loss: 0.2489 2023-03-03 15:38:36,519 - mmseg - INFO - Iter [4800/80000] lr: 1.500e-04, eta: 4:19:29, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2424, decode.acc_seg: 90.6926, loss: 0.2424 2023-03-03 15:38:46,545 - mmseg - INFO - Iter [4850/80000] lr: 1.500e-04, eta: 4:19:14, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2449, decode.acc_seg: 90.5651, loss: 0.2449 2023-03-03 15:38:56,505 - mmseg - INFO - Iter [4900/80000] lr: 1.500e-04, eta: 4:18:57, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2476, decode.acc_seg: 90.2126, loss: 0.2476 2023-03-03 15:39:06,394 - mmseg - INFO - Iter [4950/80000] lr: 1.500e-04, eta: 4:18:40, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2444, decode.acc_seg: 90.3521, loss: 0.2444 2023-03-03 15:39:16,422 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 15:39:16,422 - mmseg - INFO - Iter [5000/80000] lr: 1.500e-04, eta: 4:18:25, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2479, decode.acc_seg: 90.2190, loss: 0.2479 2023-03-03 15:39:28,982 - mmseg - INFO - Iter [5050/80000] lr: 1.500e-04, eta: 4:18:48, time: 0.251, data_time: 0.053, memory: 37833, decode.loss_ce: 0.2411, decode.acc_seg: 90.7218, loss: 0.2411 2023-03-03 15:39:39,094 - mmseg - INFO - Iter [5100/80000] lr: 1.500e-04, eta: 4:18:34, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2545, decode.acc_seg: 90.2071, loss: 0.2545 2023-03-03 15:39:49,021 - mmseg - INFO - Iter [5150/80000] lr: 1.500e-04, eta: 4:18:17, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2363, decode.acc_seg: 90.6116, loss: 0.2363 2023-03-03 15:39:58,940 - mmseg - INFO - Iter [5200/80000] lr: 1.500e-04, eta: 4:18:00, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2530, decode.acc_seg: 90.2708, loss: 0.2530 2023-03-03 15:40:09,103 - mmseg - INFO - Iter [5250/80000] lr: 1.500e-04, eta: 4:17:47, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2418, decode.acc_seg: 90.5796, loss: 0.2418 2023-03-03 15:40:19,300 - mmseg - INFO - Iter [5300/80000] lr: 1.500e-04, eta: 4:17:35, time: 0.204, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2527, decode.acc_seg: 90.1346, loss: 0.2527 2023-03-03 15:40:29,139 - mmseg - INFO - Iter [5350/80000] lr: 1.500e-04, eta: 4:17:18, time: 0.197, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2445, decode.acc_seg: 90.4675, loss: 0.2445 2023-03-03 15:40:39,203 - mmseg - INFO - Iter [5400/80000] lr: 1.500e-04, eta: 4:17:03, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2363, decode.acc_seg: 90.7892, loss: 0.2363 2023-03-03 15:40:49,103 - mmseg - INFO - Iter [5450/80000] lr: 1.500e-04, eta: 4:16:47, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2354, decode.acc_seg: 90.7422, loss: 0.2354 2023-03-03 15:40:59,043 - mmseg - INFO - Iter [5500/80000] lr: 1.500e-04, eta: 4:16:31, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2450, decode.acc_seg: 90.4323, loss: 0.2450 2023-03-03 15:41:08,965 - mmseg - INFO - Iter [5550/80000] lr: 1.500e-04, eta: 4:16:16, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2431, decode.acc_seg: 90.6153, loss: 0.2431 2023-03-03 15:41:19,041 - mmseg - INFO - Iter [5600/80000] lr: 1.500e-04, eta: 4:16:02, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2398, decode.acc_seg: 90.5780, loss: 0.2398 2023-03-03 15:41:29,203 - mmseg - INFO - Iter [5650/80000] lr: 1.500e-04, eta: 4:15:49, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2452, decode.acc_seg: 90.4365, loss: 0.2452 2023-03-03 15:41:41,825 - mmseg - INFO - Iter [5700/80000] lr: 1.500e-04, eta: 4:16:09, time: 0.253, data_time: 0.054, memory: 37833, decode.loss_ce: 0.2419, decode.acc_seg: 90.5128, loss: 0.2419 2023-03-03 15:41:51,842 - mmseg - INFO - Iter [5750/80000] lr: 1.500e-04, eta: 4:15:55, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2496, decode.acc_seg: 90.2424, loss: 0.2496 2023-03-03 15:42:02,194 - mmseg - INFO - Iter [5800/80000] lr: 1.500e-04, eta: 4:15:44, time: 0.207, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2518, decode.acc_seg: 90.2280, loss: 0.2518 2023-03-03 15:42:12,122 - mmseg - INFO - Iter [5850/80000] lr: 1.500e-04, eta: 4:15:29, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2574, decode.acc_seg: 89.8571, loss: 0.2574 2023-03-03 15:42:22,231 - mmseg - INFO - Iter [5900/80000] lr: 1.500e-04, eta: 4:15:16, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2410, decode.acc_seg: 90.4550, loss: 0.2410 2023-03-03 15:42:32,255 - mmseg - INFO - Iter [5950/80000] lr: 1.500e-04, eta: 4:15:01, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2378, decode.acc_seg: 90.7323, loss: 0.2378 2023-03-03 15:42:42,324 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 15:42:42,325 - mmseg - INFO - Iter [6000/80000] lr: 1.500e-04, eta: 4:14:48, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2405, decode.acc_seg: 90.7303, loss: 0.2405 2023-03-03 15:42:52,378 - mmseg - INFO - Iter [6050/80000] lr: 1.500e-04, eta: 4:14:34, time: 0.201, data_time: 0.007, memory: 37833, decode.loss_ce: 0.2499, decode.acc_seg: 90.0874, loss: 0.2499 2023-03-03 15:43:02,265 - mmseg - INFO - Iter [6100/80000] lr: 1.500e-04, eta: 4:14:18, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2368, decode.acc_seg: 90.6528, loss: 0.2368 2023-03-03 15:43:12,319 - mmseg - INFO - Iter [6150/80000] lr: 1.500e-04, eta: 4:14:05, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2423, decode.acc_seg: 90.6829, loss: 0.2423 2023-03-03 15:43:22,363 - mmseg - INFO - Iter [6200/80000] lr: 1.500e-04, eta: 4:13:51, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2458, decode.acc_seg: 90.5531, loss: 0.2458 2023-03-03 15:43:32,324 - mmseg - INFO - Iter [6250/80000] lr: 1.500e-04, eta: 4:13:37, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2395, decode.acc_seg: 90.7458, loss: 0.2395 2023-03-03 15:43:42,421 - mmseg - INFO - Iter [6300/80000] lr: 1.500e-04, eta: 4:13:24, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2507, decode.acc_seg: 90.2709, loss: 0.2507 2023-03-03 15:43:54,918 - mmseg - INFO - Iter [6350/80000] lr: 1.500e-04, eta: 4:13:39, time: 0.250, data_time: 0.056, memory: 37833, decode.loss_ce: 0.2343, decode.acc_seg: 90.6830, loss: 0.2343 2023-03-03 15:44:04,794 - mmseg - INFO - Iter [6400/80000] lr: 1.500e-04, eta: 4:13:23, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2311, decode.acc_seg: 90.7273, loss: 0.2311 2023-03-03 15:44:14,746 - mmseg - INFO - Iter [6450/80000] lr: 1.500e-04, eta: 4:13:09, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2403, decode.acc_seg: 90.6403, loss: 0.2403 2023-03-03 15:44:24,694 - mmseg - INFO - Iter [6500/80000] lr: 1.500e-04, eta: 4:12:54, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2368, decode.acc_seg: 90.9150, loss: 0.2368 2023-03-03 15:44:34,717 - mmseg - INFO - Iter [6550/80000] lr: 1.500e-04, eta: 4:12:40, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2458, decode.acc_seg: 90.3173, loss: 0.2458 2023-03-03 15:44:44,638 - mmseg - INFO - Iter [6600/80000] lr: 1.500e-04, eta: 4:12:26, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2454, decode.acc_seg: 90.5403, loss: 0.2454 2023-03-03 15:44:54,702 - mmseg - INFO - Iter [6650/80000] lr: 1.500e-04, eta: 4:12:12, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2503, decode.acc_seg: 90.2403, loss: 0.2503 2023-03-03 15:45:04,652 - mmseg - INFO - Iter [6700/80000] lr: 1.500e-04, eta: 4:11:58, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2461, decode.acc_seg: 90.5988, loss: 0.2461 2023-03-03 15:45:14,804 - mmseg - INFO - Iter [6750/80000] lr: 1.500e-04, eta: 4:11:46, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2542, decode.acc_seg: 90.1715, loss: 0.2542 2023-03-03 15:45:24,956 - mmseg - INFO - Iter [6800/80000] lr: 1.500e-04, eta: 4:11:34, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2446, decode.acc_seg: 90.5489, loss: 0.2446 2023-03-03 15:45:34,843 - mmseg - INFO - Iter [6850/80000] lr: 1.500e-04, eta: 4:11:19, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2451, decode.acc_seg: 90.4567, loss: 0.2451 2023-03-03 15:45:44,849 - mmseg - INFO - Iter [6900/80000] lr: 1.500e-04, eta: 4:11:06, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2416, decode.acc_seg: 90.4499, loss: 0.2416 2023-03-03 15:45:57,286 - mmseg - INFO - Iter [6950/80000] lr: 1.500e-04, eta: 4:11:18, time: 0.249, data_time: 0.054, memory: 37833, decode.loss_ce: 0.2421, decode.acc_seg: 90.4609, loss: 0.2421 2023-03-03 15:46:07,195 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 15:46:07,195 - mmseg - INFO - Iter [7000/80000] lr: 1.500e-04, eta: 4:11:03, time: 0.198, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2269, decode.acc_seg: 90.8999, loss: 0.2269 2023-03-03 15:46:17,162 - mmseg - INFO - Iter [7050/80000] lr: 1.500e-04, eta: 4:10:49, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2398, decode.acc_seg: 90.6831, loss: 0.2398 2023-03-03 15:46:27,024 - mmseg - INFO - Iter [7100/80000] lr: 1.500e-04, eta: 4:10:34, time: 0.197, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2313, decode.acc_seg: 90.8282, loss: 0.2313 2023-03-03 15:46:37,000 - mmseg - INFO - Iter [7150/80000] lr: 1.500e-04, eta: 4:10:20, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2425, decode.acc_seg: 90.5254, loss: 0.2425 2023-03-03 15:46:47,116 - mmseg - INFO - Iter [7200/80000] lr: 1.500e-04, eta: 4:10:08, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2367, decode.acc_seg: 90.6210, loss: 0.2367 2023-03-03 15:46:57,659 - mmseg - INFO - Iter [7250/80000] lr: 1.500e-04, eta: 4:10:00, time: 0.211, data_time: 0.007, memory: 37833, decode.loss_ce: 0.2377, decode.acc_seg: 90.7470, loss: 0.2377 2023-03-03 15:47:07,701 - mmseg - INFO - Iter [7300/80000] lr: 1.500e-04, eta: 4:09:47, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2425, decode.acc_seg: 90.6799, loss: 0.2425 2023-03-03 15:47:17,825 - mmseg - INFO - Iter [7350/80000] lr: 1.500e-04, eta: 4:09:35, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2433, decode.acc_seg: 90.6356, loss: 0.2433 2023-03-03 15:47:27,783 - mmseg - INFO - Iter [7400/80000] lr: 1.500e-04, eta: 4:09:21, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2431, decode.acc_seg: 90.4319, loss: 0.2431 2023-03-03 15:47:37,827 - mmseg - INFO - Iter [7450/80000] lr: 1.500e-04, eta: 4:09:09, time: 0.201, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2469, decode.acc_seg: 90.3798, loss: 0.2469 2023-03-03 15:47:47,983 - mmseg - INFO - Iter [7500/80000] lr: 1.500e-04, eta: 4:08:57, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2527, decode.acc_seg: 90.0964, loss: 0.2527 2023-03-03 15:47:58,106 - mmseg - INFO - Iter [7550/80000] lr: 1.500e-04, eta: 4:08:45, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2452, decode.acc_seg: 90.3571, loss: 0.2452 2023-03-03 15:48:10,790 - mmseg - INFO - Iter [7600/80000] lr: 1.500e-04, eta: 4:08:57, time: 0.254, data_time: 0.054, memory: 37833, decode.loss_ce: 0.2452, decode.acc_seg: 90.4140, loss: 0.2452 2023-03-03 15:48:20,766 - mmseg - INFO - Iter [7650/80000] lr: 1.500e-04, eta: 4:08:44, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2448, decode.acc_seg: 90.4015, loss: 0.2448 2023-03-03 15:48:30,860 - mmseg - INFO - Iter [7700/80000] lr: 1.500e-04, eta: 4:08:31, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2323, decode.acc_seg: 90.7999, loss: 0.2323 2023-03-03 15:48:40,856 - mmseg - INFO - Iter [7750/80000] lr: 1.500e-04, eta: 4:08:18, time: 0.200, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2494, decode.acc_seg: 90.2294, loss: 0.2494 2023-03-03 15:48:50,945 - mmseg - INFO - Iter [7800/80000] lr: 1.500e-04, eta: 4:08:06, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2354, decode.acc_seg: 90.7632, loss: 0.2354 2023-03-03 15:49:01,088 - mmseg - INFO - Iter [7850/80000] lr: 1.500e-04, eta: 4:07:54, time: 0.203, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2401, decode.acc_seg: 90.5642, loss: 0.2401 2023-03-03 15:49:11,172 - mmseg - INFO - Iter [7900/80000] lr: 1.500e-04, eta: 4:07:42, time: 0.202, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2353, decode.acc_seg: 90.7558, loss: 0.2353 2023-03-03 15:49:21,098 - mmseg - INFO - Iter [7950/80000] lr: 1.500e-04, eta: 4:07:28, time: 0.199, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2478, decode.acc_seg: 90.4452, loss: 0.2478 2023-03-03 15:49:31,116 - mmseg - INFO - Saving checkpoint at 8000 iterations 2023-03-03 15:49:31,993 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 15:49:31,993 - mmseg - INFO - Iter [8000/80000] lr: 1.500e-04, eta: 4:07:23, time: 0.218, data_time: 0.006, memory: 37833, decode.loss_ce: 0.2516, decode.acc_seg: 90.3974, loss: 0.2516 2023-03-03 16:04:32,482 - mmseg - INFO - per class results: 2023-03-03 16:04:32,488 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 74.41 | 88.47 | | building | 81.88 | 92.26 | | sky | 93.71 | 95.79 | | floor | 78.15 | 90.51 | | tree | 72.71 | 89.31 | | ceiling | 81.68 | 90.44 | | road | 80.72 | 88.5 | | bed | 86.78 | 95.67 | | windowpane | 59.13 | 75.45 | | grass | 65.11 | 82.42 | | cabinet | 58.58 | 72.33 | | sidewalk | 63.64 | 77.71 | | person | 77.14 | 88.22 | | earth | 31.43 | 45.48 | | door | 45.45 | 65.0 | | table | 58.76 | 76.23 | | mountain | 50.06 | 64.77 | | plant | 50.66 | 65.84 | | curtain | 70.52 | 82.37 | | chair | 53.7 | 68.24 | | car | 81.01 | 88.89 | | water | 44.28 | 59.58 | | painting | 71.77 | 85.24 | | sofa | 61.8 | 73.73 | | shelf | 34.55 | 43.56 | | house | 45.54 | 66.65 | | sea | 41.32 | 70.88 | | mirror | 61.94 | 69.34 | | rug | 51.45 | 57.93 | | field | 22.22 | 33.69 | | armchair | 40.81 | 67.53 | | seat | 58.68 | 73.37 | | fence | 33.52 | 45.18 | | desk | 45.99 | 70.2 | | rock | 30.48 | 46.29 | | wardrobe | 44.84 | 54.31 | | lamp | 61.37 | 74.08 | | bathtub | 74.61 | 84.51 | | railing | 27.61 | 40.19 | | cushion | 51.42 | 65.2 | | base | 21.15 | 33.75 | | box | 22.87 | 32.98 | | column | 42.91 | 52.04 | | signboard | 33.07 | 39.89 | | chest of drawers | 33.0 | 42.99 | | counter | 26.79 | 32.54 | | sand | 29.13 | 49.57 | | sink | 66.77 | 76.24 | | skyscraper | 59.81 | 66.58 | | fireplace | 69.48 | 85.77 | | refrigerator | 70.74 | 81.63 | | grandstand | 37.57 | 57.01 | | path | 13.69 | 19.04 | | stairs | 29.78 | 34.13 | | runway | 60.71 | 80.72 | | case | 45.53 | 72.59 | | pool table | 91.38 | 95.4 | | pillow | 49.16 | 56.9 | | screen door | 59.28 | 66.24 | | stairway | 29.82 | 38.34 | | river | 12.06 | 20.8 | | bridge | 57.26 | 60.97 | | bookcase | 35.8 | 43.51 | | blind | 38.41 | 44.07 | | coffee table | 57.91 | 72.75 | | toilet | 83.44 | 90.38 | | flower | 30.82 | 35.88 | | book | 44.42 | 63.45 | | hill | 5.29 | 6.78 | | bench | 37.15 | 47.81 | | countertop | 52.38 | 69.77 | | stove | 70.5 | 81.86 | | palm | 46.8 | 58.64 | | kitchen island | 43.69 | 73.25 | | computer | 54.32 | 64.42 | | swivel chair | 45.44 | 62.88 | | boat | 47.6 | 56.51 | | bar | 25.22 | 30.36 | | arcade machine | 21.07 | 22.25 | | hovel | 28.46 | 29.49 | | bus | 77.25 | 84.16 | | towel | 51.98 | 66.84 | | light | 48.36 | 52.2 | | truck | 28.18 | 36.71 | | tower | 28.42 | 34.42 | | chandelier | 66.32 | 84.91 | | awning | 25.29 | 30.65 | | streetlight | 25.49 | 34.5 | | booth | 37.2 | 41.21 | | television receiver | 67.68 | 76.48 | | airplane | 51.29 | 60.59 | | dirt track | 4.41 | 12.73 | | apparel | 24.74 | 29.79 | | pole | 22.97 | 33.28 | | land | 1.1 | 1.81 | | bannister | 7.59 | 10.12 | | escalator | 14.7 | 14.95 | | ottoman | 41.83 | 47.67 | | bottle | 12.21 | 19.59 | | buffet | 33.68 | 40.1 | | poster | 24.02 | 28.26 | | stage | 8.2 | 10.03 | | van | 40.17 | 57.13 | | ship | 66.8 | 76.93 | | fountain | 0.39 | 0.39 | | conveyer belt | 72.1 | 86.16 | | canopy | 14.1 | 15.59 | | washer | 63.02 | 64.1 | | plaything | 12.81 | 14.21 | | swimming pool | 30.54 | 37.26 | | stool | 37.84 | 54.57 | | barrel | 45.79 | 65.06 | | basket | 19.61 | 36.89 | | waterfall | 56.68 | 72.94 | | tent | 94.02 | 97.75 | | bag | 7.0 | 8.05 | | minibike | 51.45 | 61.47 | | cradle | 74.39 | 98.61 | | oven | 20.71 | 49.22 | | ball | 46.99 | 61.34 | | food | 46.97 | 55.01 | | step | 2.25 | 2.51 | | tank | 37.85 | 38.2 | | trade name | 22.39 | 25.59 | | microwave | 40.67 | 44.65 | | pot | 33.67 | 38.6 | | animal | 43.57 | 43.96 | | bicycle | 43.82 | 67.05 | | lake | 61.24 | 62.6 | | dishwasher | 69.9 | 74.04 | | screen | 56.36 | 68.39 | | blanket | 3.49 | 3.72 | | sculpture | 37.04 | 53.84 | | hood | 56.33 | 67.03 | | sconce | 38.91 | 45.05 | | vase | 31.97 | 40.93 | | traffic light | 26.08 | 37.52 | | tray | 1.99 | 2.25 | | ashcan | 36.67 | 59.91 | | fan | 55.41 | 72.25 | | pier | 20.39 | 30.17 | | crt screen | 4.33 | 7.76 | | plate | 34.69 | 45.95 | | monitor | 66.69 | 77.96 | | bulletin board | 36.0 | 52.76 | | shower | 1.41 | 3.3 | | radiator | 41.39 | 50.23 | | glass | 8.67 | 9.48 | | clock | 13.45 | 14.97 | | flag | 36.2 | 39.9 | +---------------------+-------+-------+ 2023-03-03 16:04:32,488 - mmseg - INFO - Summary: 2023-03-03 16:04:32,488 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 80.93 | 43.45 | 53.73 | +-------+-------+-------+ 2023-03-03 16:04:33,318 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_8000.pth. 2023-03-03 16:04:33,318 - mmseg - INFO - Best mIoU is 0.4345 at 8000 iter. 2023-03-03 16:04:33,318 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:04:33,319 - mmseg - INFO - Iter(val) [250] aAcc: 0.8093, mIoU: 0.4345, mAcc: 0.5373, IoU.background: nan, IoU.wall: 0.7441, IoU.building: 0.8188, IoU.sky: 0.9371, IoU.floor: 0.7815, IoU.tree: 0.7271, IoU.ceiling: 0.8168, IoU.road: 0.8072, IoU.bed : 0.8678, IoU.windowpane: 0.5913, IoU.grass: 0.6511, IoU.cabinet: 0.5858, IoU.sidewalk: 0.6364, IoU.person: 0.7714, IoU.earth: 0.3143, IoU.door: 0.4545, IoU.table: 0.5876, IoU.mountain: 0.5006, IoU.plant: 0.5066, IoU.curtain: 0.7052, IoU.chair: 0.5370, IoU.car: 0.8101, IoU.water: 0.4428, IoU.painting: 0.7177, IoU.sofa: 0.6180, IoU.shelf: 0.3455, IoU.house: 0.4554, IoU.sea: 0.4132, IoU.mirror: 0.6194, IoU.rug: 0.5145, IoU.field: 0.2222, IoU.armchair: 0.4081, IoU.seat: 0.5868, IoU.fence: 0.3352, IoU.desk: 0.4599, IoU.rock: 0.3048, IoU.wardrobe: 0.4484, IoU.lamp: 0.6137, IoU.bathtub: 0.7461, IoU.railing: 0.2761, IoU.cushion: 0.5142, IoU.base: 0.2115, IoU.box: 0.2287, IoU.column: 0.4291, IoU.signboard: 0.3307, IoU.chest of drawers: 0.3300, IoU.counter: 0.2679, IoU.sand: 0.2913, IoU.sink: 0.6677, IoU.skyscraper: 0.5981, IoU.fireplace: 0.6948, IoU.refrigerator: 0.7074, IoU.grandstand: 0.3757, IoU.path: 0.1369, IoU.stairs: 0.2978, IoU.runway: 0.6071, IoU.case: 0.4553, IoU.pool table: 0.9138, IoU.pillow: 0.4916, IoU.screen door: 0.5928, IoU.stairway: 0.2982, IoU.river: 0.1206, IoU.bridge: 0.5726, IoU.bookcase: 0.3580, IoU.blind: 0.3841, IoU.coffee table: 0.5791, IoU.toilet: 0.8344, IoU.flower: 0.3082, IoU.book: 0.4442, IoU.hill: 0.0529, IoU.bench: 0.3715, IoU.countertop: 0.5238, IoU.stove: 0.7050, IoU.palm: 0.4680, IoU.kitchen island: 0.4369, IoU.computer: 0.5432, IoU.swivel chair: 0.4544, IoU.boat: 0.4760, IoU.bar: 0.2522, IoU.arcade machine: 0.2107, IoU.hovel: 0.2846, IoU.bus: 0.7725, IoU.towel: 0.5198, IoU.light: 0.4836, IoU.truck: 0.2818, IoU.tower: 0.2842, IoU.chandelier: 0.6632, IoU.awning: 0.2529, IoU.streetlight: 0.2549, IoU.booth: 0.3720, IoU.television receiver: 0.6768, IoU.airplane: 0.5129, IoU.dirt track: 0.0441, IoU.apparel: 0.2474, IoU.pole: 0.2297, IoU.land: 0.0110, IoU.bannister: 0.0759, IoU.escalator: 0.1470, IoU.ottoman: 0.4183, IoU.bottle: 0.1221, IoU.buffet: 0.3368, IoU.poster: 0.2402, IoU.stage: 0.0820, IoU.van: 0.4017, IoU.ship: 0.6680, IoU.fountain: 0.0039, IoU.conveyer belt: 0.7210, IoU.canopy: 0.1410, IoU.washer: 0.6302, IoU.plaything: 0.1281, IoU.swimming pool: 0.3054, IoU.stool: 0.3784, IoU.barrel: 0.4579, IoU.basket: 0.1961, IoU.waterfall: 0.5668, IoU.tent: 0.9402, IoU.bag: 0.0700, IoU.minibike: 0.5145, IoU.cradle: 0.7439, IoU.oven: 0.2071, IoU.ball: 0.4699, IoU.food: 0.4697, IoU.step: 0.0225, IoU.tank: 0.3785, IoU.trade name: 0.2239, IoU.microwave: 0.4067, IoU.pot: 0.3367, IoU.animal: 0.4357, IoU.bicycle: 0.4382, IoU.lake: 0.6124, IoU.dishwasher: 0.6990, IoU.screen: 0.5636, IoU.blanket: 0.0349, IoU.sculpture: 0.3704, IoU.hood: 0.5633, IoU.sconce: 0.3891, IoU.vase: 0.3197, IoU.traffic light: 0.2608, IoU.tray: 0.0199, IoU.ashcan: 0.3667, IoU.fan: 0.5541, IoU.pier: 0.2039, IoU.crt screen: 0.0433, IoU.plate: 0.3469, IoU.monitor: 0.6669, IoU.bulletin board: 0.3600, IoU.shower: 0.0141, IoU.radiator: 0.4139, IoU.glass: 0.0867, IoU.clock: 0.1345, IoU.flag: 0.3620, Acc.background: nan, Acc.wall: 0.8847, Acc.building: 0.9226, Acc.sky: 0.9579, Acc.floor: 0.9051, Acc.tree: 0.8931, Acc.ceiling: 0.9044, Acc.road: 0.8850, Acc.bed : 0.9567, Acc.windowpane: 0.7545, Acc.grass: 0.8242, Acc.cabinet: 0.7233, Acc.sidewalk: 0.7771, Acc.person: 0.8822, Acc.earth: 0.4548, Acc.door: 0.6500, Acc.table: 0.7623, Acc.mountain: 0.6477, Acc.plant: 0.6584, Acc.curtain: 0.8237, Acc.chair: 0.6824, Acc.car: 0.8889, Acc.water: 0.5958, Acc.painting: 0.8524, Acc.sofa: 0.7373, Acc.shelf: 0.4356, Acc.house: 0.6665, Acc.sea: 0.7088, Acc.mirror: 0.6934, Acc.rug: 0.5793, Acc.field: 0.3369, Acc.armchair: 0.6753, Acc.seat: 0.7337, Acc.fence: 0.4518, Acc.desk: 0.7020, Acc.rock: 0.4629, Acc.wardrobe: 0.5431, Acc.lamp: 0.7408, Acc.bathtub: 0.8451, Acc.railing: 0.4019, Acc.cushion: 0.6520, Acc.base: 0.3375, Acc.box: 0.3298, Acc.column: 0.5204, Acc.signboard: 0.3989, Acc.chest of drawers: 0.4299, Acc.counter: 0.3254, Acc.sand: 0.4957, Acc.sink: 0.7624, Acc.skyscraper: 0.6658, Acc.fireplace: 0.8577, Acc.refrigerator: 0.8163, Acc.grandstand: 0.5701, Acc.path: 0.1904, Acc.stairs: 0.3413, Acc.runway: 0.8072, Acc.case: 0.7259, Acc.pool table: 0.9540, Acc.pillow: 0.5690, Acc.screen door: 0.6624, Acc.stairway: 0.3834, Acc.river: 0.2080, Acc.bridge: 0.6097, Acc.bookcase: 0.4351, Acc.blind: 0.4407, Acc.coffee table: 0.7275, Acc.toilet: 0.9038, Acc.flower: 0.3588, Acc.book: 0.6345, Acc.hill: 0.0678, Acc.bench: 0.4781, Acc.countertop: 0.6977, Acc.stove: 0.8186, Acc.palm: 0.5864, Acc.kitchen island: 0.7325, Acc.computer: 0.6442, Acc.swivel chair: 0.6288, Acc.boat: 0.5651, Acc.bar: 0.3036, Acc.arcade machine: 0.2225, Acc.hovel: 0.2949, Acc.bus: 0.8416, Acc.towel: 0.6684, Acc.light: 0.5220, Acc.truck: 0.3671, Acc.tower: 0.3442, Acc.chandelier: 0.8491, Acc.awning: 0.3065, Acc.streetlight: 0.3450, Acc.booth: 0.4121, Acc.television receiver: 0.7648, Acc.airplane: 0.6059, Acc.dirt track: 0.1273, Acc.apparel: 0.2979, Acc.pole: 0.3328, Acc.land: 0.0181, Acc.bannister: 0.1012, Acc.escalator: 0.1495, Acc.ottoman: 0.4767, Acc.bottle: 0.1959, Acc.buffet: 0.4010, Acc.poster: 0.2826, Acc.stage: 0.1003, Acc.van: 0.5713, Acc.ship: 0.7693, Acc.fountain: 0.0039, Acc.conveyer belt: 0.8616, Acc.canopy: 0.1559, Acc.washer: 0.6410, Acc.plaything: 0.1421, Acc.swimming pool: 0.3726, Acc.stool: 0.5457, Acc.barrel: 0.6506, Acc.basket: 0.3689, Acc.waterfall: 0.7294, Acc.tent: 0.9775, Acc.bag: 0.0805, Acc.minibike: 0.6147, Acc.cradle: 0.9861, Acc.oven: 0.4922, Acc.ball: 0.6134, Acc.food: 0.5501, Acc.step: 0.0251, Acc.tank: 0.3820, Acc.trade name: 0.2559, Acc.microwave: 0.4465, Acc.pot: 0.3860, Acc.animal: 0.4396, Acc.bicycle: 0.6705, Acc.lake: 0.6260, Acc.dishwasher: 0.7404, Acc.screen: 0.6839, Acc.blanket: 0.0372, Acc.sculpture: 0.5384, Acc.hood: 0.6703, Acc.sconce: 0.4505, Acc.vase: 0.4093, Acc.traffic light: 0.3752, Acc.tray: 0.0225, Acc.ashcan: 0.5991, Acc.fan: 0.7225, Acc.pier: 0.3017, Acc.crt screen: 0.0776, Acc.plate: 0.4595, Acc.monitor: 0.7796, Acc.bulletin board: 0.5276, Acc.shower: 0.0330, Acc.radiator: 0.5023, Acc.glass: 0.0948, Acc.clock: 0.1497, Acc.flag: 0.3990 2023-03-03 16:04:43,603 - mmseg - INFO - Iter [8050/80000] lr: 1.500e-04, eta: 6:21:28, time: 18.232, data_time: 18.034, memory: 67202, decode.loss_ce: 0.2360, decode.acc_seg: 90.8098, loss: 0.2360 2023-03-03 16:04:53,774 - mmseg - INFO - Iter [8100/80000] lr: 1.500e-04, eta: 6:20:21, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2407, decode.acc_seg: 90.6144, loss: 0.2407 2023-03-03 16:05:03,835 - mmseg - INFO - Iter [8150/80000] lr: 1.500e-04, eta: 6:19:14, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2436, decode.acc_seg: 90.5538, loss: 0.2436 2023-03-03 16:05:13,809 - mmseg - INFO - Iter [8200/80000] lr: 1.500e-04, eta: 6:18:07, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2403, decode.acc_seg: 90.4742, loss: 0.2403 2023-03-03 16:05:26,473 - mmseg - INFO - Iter [8250/80000] lr: 1.500e-04, eta: 6:17:24, time: 0.253, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2383, decode.acc_seg: 90.6596, loss: 0.2383 2023-03-03 16:05:36,589 - mmseg - INFO - Iter [8300/80000] lr: 1.500e-04, eta: 6:16:19, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2372, decode.acc_seg: 90.7305, loss: 0.2372 2023-03-03 16:05:46,632 - mmseg - INFO - Iter [8350/80000] lr: 1.500e-04, eta: 6:15:15, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2361, decode.acc_seg: 90.6943, loss: 0.2361 2023-03-03 16:05:56,708 - mmseg - INFO - Iter [8400/80000] lr: 1.500e-04, eta: 6:14:11, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2429, decode.acc_seg: 90.4218, loss: 0.2429 2023-03-03 16:06:06,772 - mmseg - INFO - Iter [8450/80000] lr: 1.500e-04, eta: 6:13:08, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2403, decode.acc_seg: 90.4727, loss: 0.2403 2023-03-03 16:06:16,949 - mmseg - INFO - Iter [8500/80000] lr: 1.500e-04, eta: 6:12:06, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2354, decode.acc_seg: 90.7745, loss: 0.2354 2023-03-03 16:06:26,993 - mmseg - INFO - Iter [8550/80000] lr: 1.500e-04, eta: 6:11:04, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2372, decode.acc_seg: 90.5987, loss: 0.2372 2023-03-03 16:06:36,923 - mmseg - INFO - Iter [8600/80000] lr: 1.500e-04, eta: 6:10:01, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2356, decode.acc_seg: 90.7652, loss: 0.2356 2023-03-03 16:06:46,970 - mmseg - INFO - Iter [8650/80000] lr: 1.500e-04, eta: 6:09:00, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2398, decode.acc_seg: 90.5784, loss: 0.2398 2023-03-03 16:06:56,945 - mmseg - INFO - Iter [8700/80000] lr: 1.500e-04, eta: 6:08:00, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2388, decode.acc_seg: 90.6737, loss: 0.2388 2023-03-03 16:07:06,854 - mmseg - INFO - Iter [8750/80000] lr: 1.500e-04, eta: 6:06:59, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2444, decode.acc_seg: 90.5774, loss: 0.2444 2023-03-03 16:07:16,852 - mmseg - INFO - Iter [8800/80000] lr: 1.500e-04, eta: 6:05:59, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2416, decode.acc_seg: 90.4747, loss: 0.2416 2023-03-03 16:07:29,444 - mmseg - INFO - Iter [8850/80000] lr: 1.500e-04, eta: 6:05:21, time: 0.252, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2441, decode.acc_seg: 90.3441, loss: 0.2441 2023-03-03 16:07:39,745 - mmseg - INFO - Iter [8900/80000] lr: 1.500e-04, eta: 6:04:25, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2392, decode.acc_seg: 90.6259, loss: 0.2392 2023-03-03 16:07:49,700 - mmseg - INFO - Iter [8950/80000] lr: 1.500e-04, eta: 6:03:26, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2373, decode.acc_seg: 90.5935, loss: 0.2373 2023-03-03 16:07:59,818 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:07:59,819 - mmseg - INFO - Iter [9000/80000] lr: 1.500e-04, eta: 6:02:30, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2342, decode.acc_seg: 90.7772, loss: 0.2342 2023-03-03 16:08:09,922 - mmseg - INFO - Iter [9050/80000] lr: 1.500e-04, eta: 6:01:33, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2384, decode.acc_seg: 90.7706, loss: 0.2384 2023-03-03 16:08:20,097 - mmseg - INFO - Iter [9100/80000] lr: 1.500e-04, eta: 6:00:38, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2485, decode.acc_seg: 90.2982, loss: 0.2485 2023-03-03 16:08:30,162 - mmseg - INFO - Iter [9150/80000] lr: 1.500e-04, eta: 5:59:43, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2436, decode.acc_seg: 90.3806, loss: 0.2436 2023-03-03 16:08:40,148 - mmseg - INFO - Iter [9200/80000] lr: 1.500e-04, eta: 5:58:47, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2422, decode.acc_seg: 90.5847, loss: 0.2422 2023-03-03 16:08:50,194 - mmseg - INFO - Iter [9250/80000] lr: 1.500e-04, eta: 5:57:53, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2416, decode.acc_seg: 90.6029, loss: 0.2416 2023-03-03 16:09:00,249 - mmseg - INFO - Iter [9300/80000] lr: 1.500e-04, eta: 5:56:59, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2395, decode.acc_seg: 90.5398, loss: 0.2395 2023-03-03 16:09:10,268 - mmseg - INFO - Iter [9350/80000] lr: 1.500e-04, eta: 5:56:05, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2386, decode.acc_seg: 90.7506, loss: 0.2386 2023-03-03 16:09:20,252 - mmseg - INFO - Iter [9400/80000] lr: 1.500e-04, eta: 5:55:11, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2353, decode.acc_seg: 90.9324, loss: 0.2353 2023-03-03 16:09:30,336 - mmseg - INFO - Iter [9450/80000] lr: 1.500e-04, eta: 5:54:18, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2337, decode.acc_seg: 90.6908, loss: 0.2337 2023-03-03 16:09:42,762 - mmseg - INFO - Iter [9500/80000] lr: 1.500e-04, eta: 5:53:44, time: 0.249, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2303, decode.acc_seg: 90.8421, loss: 0.2303 2023-03-03 16:09:52,763 - mmseg - INFO - Iter [9550/80000] lr: 1.500e-04, eta: 5:52:51, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2351, decode.acc_seg: 90.7353, loss: 0.2351 2023-03-03 16:10:02,996 - mmseg - INFO - Iter [9600/80000] lr: 1.500e-04, eta: 5:52:01, time: 0.205, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2298, decode.acc_seg: 91.0304, loss: 0.2298 2023-03-03 16:10:13,057 - mmseg - INFO - Iter [9650/80000] lr: 1.500e-04, eta: 5:51:10, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2471, decode.acc_seg: 90.3303, loss: 0.2471 2023-03-03 16:10:23,223 - mmseg - INFO - Iter [9700/80000] lr: 1.500e-04, eta: 5:50:20, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2382, decode.acc_seg: 90.7302, loss: 0.2382 2023-03-03 16:10:33,245 - mmseg - INFO - Iter [9750/80000] lr: 1.500e-04, eta: 5:49:30, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2399, decode.acc_seg: 90.5420, loss: 0.2399 2023-03-03 16:10:43,218 - mmseg - INFO - Iter [9800/80000] lr: 1.500e-04, eta: 5:48:40, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2364, decode.acc_seg: 90.6469, loss: 0.2364 2023-03-03 16:10:53,272 - mmseg - INFO - Iter [9850/80000] lr: 1.500e-04, eta: 5:47:50, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2380, decode.acc_seg: 90.8182, loss: 0.2380 2023-03-03 16:11:03,250 - mmseg - INFO - Iter [9900/80000] lr: 1.500e-04, eta: 5:47:01, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2384, decode.acc_seg: 90.7513, loss: 0.2384 2023-03-03 16:11:13,477 - mmseg - INFO - Iter [9950/80000] lr: 1.500e-04, eta: 5:46:13, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2504, decode.acc_seg: 90.2921, loss: 0.2504 2023-03-03 16:11:23,476 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:11:23,476 - mmseg - INFO - Iter [10000/80000] lr: 1.500e-04, eta: 5:45:25, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2396, decode.acc_seg: 90.6284, loss: 0.2396 2023-03-03 16:11:33,453 - mmseg - INFO - Iter [10050/80000] lr: 7.500e-05, eta: 5:44:36, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2375, decode.acc_seg: 90.7940, loss: 0.2375 2023-03-03 16:11:45,930 - mmseg - INFO - Iter [10100/80000] lr: 7.500e-05, eta: 5:44:05, time: 0.250, data_time: 0.052, memory: 67202, decode.loss_ce: 0.2367, decode.acc_seg: 90.6708, loss: 0.2367 2023-03-03 16:11:55,865 - mmseg - INFO - Iter [10150/80000] lr: 7.500e-05, eta: 5:43:17, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2264, decode.acc_seg: 90.9038, loss: 0.2264 2023-03-03 16:12:05,787 - mmseg - INFO - Iter [10200/80000] lr: 7.500e-05, eta: 5:42:30, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2222, decode.acc_seg: 91.1626, loss: 0.2222 2023-03-03 16:12:15,817 - mmseg - INFO - Iter [10250/80000] lr: 7.500e-05, eta: 5:41:43, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2362, decode.acc_seg: 90.7762, loss: 0.2362 2023-03-03 16:12:25,852 - mmseg - INFO - Iter [10300/80000] lr: 7.500e-05, eta: 5:40:57, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2343, decode.acc_seg: 90.6866, loss: 0.2343 2023-03-03 16:12:35,772 - mmseg - INFO - Iter [10350/80000] lr: 7.500e-05, eta: 5:40:10, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2277, decode.acc_seg: 90.8685, loss: 0.2277 2023-03-03 16:12:45,838 - mmseg - INFO - Iter [10400/80000] lr: 7.500e-05, eta: 5:39:25, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2399, decode.acc_seg: 90.4983, loss: 0.2399 2023-03-03 16:12:55,999 - mmseg - INFO - Iter [10450/80000] lr: 7.500e-05, eta: 5:38:40, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2319, decode.acc_seg: 90.8804, loss: 0.2319 2023-03-03 16:13:06,091 - mmseg - INFO - Iter [10500/80000] lr: 7.500e-05, eta: 5:37:56, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2325, decode.acc_seg: 90.8020, loss: 0.2325 2023-03-03 16:13:16,074 - mmseg - INFO - Iter [10550/80000] lr: 7.500e-05, eta: 5:37:11, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2370, decode.acc_seg: 90.6258, loss: 0.2370 2023-03-03 16:13:26,002 - mmseg - INFO - Iter [10600/80000] lr: 7.500e-05, eta: 5:36:26, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2243, decode.acc_seg: 91.1414, loss: 0.2243 2023-03-03 16:13:36,202 - mmseg - INFO - Iter [10650/80000] lr: 7.500e-05, eta: 5:35:43, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2210, decode.acc_seg: 91.3157, loss: 0.2210 2023-03-03 16:13:46,335 - mmseg - INFO - Iter [10700/80000] lr: 7.500e-05, eta: 5:35:00, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2353, decode.acc_seg: 90.7556, loss: 0.2353 2023-03-03 16:13:58,737 - mmseg - INFO - Iter [10750/80000] lr: 7.500e-05, eta: 5:34:32, time: 0.248, data_time: 0.052, memory: 67202, decode.loss_ce: 0.2388, decode.acc_seg: 90.7495, loss: 0.2388 2023-03-03 16:14:08,751 - mmseg - INFO - Iter [10800/80000] lr: 7.500e-05, eta: 5:33:49, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2276, decode.acc_seg: 90.9939, loss: 0.2276 2023-03-03 16:14:18,713 - mmseg - INFO - Iter [10850/80000] lr: 7.500e-05, eta: 5:33:06, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2369, decode.acc_seg: 90.7584, loss: 0.2369 2023-03-03 16:14:28,837 - mmseg - INFO - Iter [10900/80000] lr: 7.500e-05, eta: 5:32:24, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2434, decode.acc_seg: 90.3444, loss: 0.2434 2023-03-03 16:14:38,809 - mmseg - INFO - Iter [10950/80000] lr: 7.500e-05, eta: 5:31:41, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2298, decode.acc_seg: 90.9667, loss: 0.2298 2023-03-03 16:14:48,892 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:14:48,892 - mmseg - INFO - Iter [11000/80000] lr: 7.500e-05, eta: 5:31:00, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2257, decode.acc_seg: 91.0782, loss: 0.2257 2023-03-03 16:14:58,964 - mmseg - INFO - Iter [11050/80000] lr: 7.500e-05, eta: 5:30:18, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2289, decode.acc_seg: 90.9275, loss: 0.2289 2023-03-03 16:15:08,866 - mmseg - INFO - Iter [11100/80000] lr: 7.500e-05, eta: 5:29:36, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2343, decode.acc_seg: 90.7009, loss: 0.2343 2023-03-03 16:15:18,943 - mmseg - INFO - Iter [11150/80000] lr: 7.500e-05, eta: 5:28:56, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2257, decode.acc_seg: 91.0055, loss: 0.2257 2023-03-03 16:15:28,963 - mmseg - INFO - Iter [11200/80000] lr: 7.500e-05, eta: 5:28:15, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2276, decode.acc_seg: 90.9634, loss: 0.2276 2023-03-03 16:15:38,969 - mmseg - INFO - Iter [11250/80000] lr: 7.500e-05, eta: 5:27:34, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2244, decode.acc_seg: 91.1245, loss: 0.2244 2023-03-03 16:15:49,011 - mmseg - INFO - Iter [11300/80000] lr: 7.500e-05, eta: 5:26:54, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2242, decode.acc_seg: 91.0137, loss: 0.2242 2023-03-03 16:15:59,284 - mmseg - INFO - Iter [11350/80000] lr: 7.500e-05, eta: 5:26:15, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2268, decode.acc_seg: 91.0518, loss: 0.2268 2023-03-03 16:16:11,759 - mmseg - INFO - Iter [11400/80000] lr: 7.500e-05, eta: 5:25:50, time: 0.249, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2430, decode.acc_seg: 90.5892, loss: 0.2430 2023-03-03 16:16:21,734 - mmseg - INFO - Iter [11450/80000] lr: 7.500e-05, eta: 5:25:11, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2353, decode.acc_seg: 90.7473, loss: 0.2353 2023-03-03 16:16:31,800 - mmseg - INFO - Iter [11500/80000] lr: 7.500e-05, eta: 5:24:32, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2187, decode.acc_seg: 91.3593, loss: 0.2187 2023-03-03 16:16:41,874 - mmseg - INFO - Iter [11550/80000] lr: 7.500e-05, eta: 5:23:53, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2252, decode.acc_seg: 91.1169, loss: 0.2252 2023-03-03 16:16:51,938 - mmseg - INFO - Iter [11600/80000] lr: 7.500e-05, eta: 5:23:14, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2241, decode.acc_seg: 91.0721, loss: 0.2241 2023-03-03 16:17:01,991 - mmseg - INFO - Iter [11650/80000] lr: 7.500e-05, eta: 5:22:36, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2274, decode.acc_seg: 90.9592, loss: 0.2274 2023-03-03 16:17:11,961 - mmseg - INFO - Iter [11700/80000] lr: 7.500e-05, eta: 5:21:57, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2352, decode.acc_seg: 90.6731, loss: 0.2352 2023-03-03 16:17:21,927 - mmseg - INFO - Iter [11750/80000] lr: 7.500e-05, eta: 5:21:19, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2293, decode.acc_seg: 91.1243, loss: 0.2293 2023-03-03 16:17:31,909 - mmseg - INFO - Iter [11800/80000] lr: 7.500e-05, eta: 5:20:41, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2258, decode.acc_seg: 90.9804, loss: 0.2258 2023-03-03 16:17:41,800 - mmseg - INFO - Iter [11850/80000] lr: 7.500e-05, eta: 5:20:02, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2295, decode.acc_seg: 90.8322, loss: 0.2295 2023-03-03 16:17:51,775 - mmseg - INFO - Iter [11900/80000] lr: 7.500e-05, eta: 5:19:25, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2346, decode.acc_seg: 90.7956, loss: 0.2346 2023-03-03 16:18:01,783 - mmseg - INFO - Iter [11950/80000] lr: 7.500e-05, eta: 5:18:48, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2298, decode.acc_seg: 91.0679, loss: 0.2298 2023-03-03 16:18:14,320 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:18:14,320 - mmseg - INFO - Iter [12000/80000] lr: 7.500e-05, eta: 5:18:25, time: 0.251, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2286, decode.acc_seg: 90.7859, loss: 0.2286 2023-03-03 16:18:24,476 - mmseg - INFO - Iter [12050/80000] lr: 7.500e-05, eta: 5:17:49, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2265, decode.acc_seg: 91.0565, loss: 0.2265 2023-03-03 16:18:34,411 - mmseg - INFO - Iter [12100/80000] lr: 7.500e-05, eta: 5:17:12, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2342, decode.acc_seg: 90.9549, loss: 0.2342 2023-03-03 16:18:44,479 - mmseg - INFO - Iter [12150/80000] lr: 7.500e-05, eta: 5:16:36, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2295, decode.acc_seg: 91.1003, loss: 0.2295 2023-03-03 16:18:54,476 - mmseg - INFO - Iter [12200/80000] lr: 7.500e-05, eta: 5:16:00, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2272, decode.acc_seg: 90.8436, loss: 0.2272 2023-03-03 16:19:04,462 - mmseg - INFO - Iter [12250/80000] lr: 7.500e-05, eta: 5:15:24, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2335, decode.acc_seg: 90.8194, loss: 0.2335 2023-03-03 16:19:14,391 - mmseg - INFO - Iter [12300/80000] lr: 7.500e-05, eta: 5:14:47, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2241, decode.acc_seg: 91.0766, loss: 0.2241 2023-03-03 16:19:24,442 - mmseg - INFO - Iter [12350/80000] lr: 7.500e-05, eta: 5:14:12, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2308, decode.acc_seg: 90.8590, loss: 0.2308 2023-03-03 16:19:34,427 - mmseg - INFO - Iter [12400/80000] lr: 7.500e-05, eta: 5:13:37, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2351, decode.acc_seg: 90.9147, loss: 0.2351 2023-03-03 16:19:44,610 - mmseg - INFO - Iter [12450/80000] lr: 7.500e-05, eta: 5:13:02, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2291, decode.acc_seg: 90.9418, loss: 0.2291 2023-03-03 16:19:54,687 - mmseg - INFO - Iter [12500/80000] lr: 7.500e-05, eta: 5:12:28, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2287, decode.acc_seg: 91.1211, loss: 0.2287 2023-03-03 16:20:04,732 - mmseg - INFO - Iter [12550/80000] lr: 7.500e-05, eta: 5:11:53, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2306, decode.acc_seg: 90.9542, loss: 0.2306 2023-03-03 16:20:14,795 - mmseg - INFO - Iter [12600/80000] lr: 7.500e-05, eta: 5:11:19, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2263, decode.acc_seg: 90.9862, loss: 0.2263 2023-03-03 16:20:27,193 - mmseg - INFO - Iter [12650/80000] lr: 7.500e-05, eta: 5:10:57, time: 0.248, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2314, decode.acc_seg: 90.8264, loss: 0.2314 2023-03-03 16:20:37,153 - mmseg - INFO - Iter [12700/80000] lr: 7.500e-05, eta: 5:10:23, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2273, decode.acc_seg: 91.0464, loss: 0.2273 2023-03-03 16:20:47,133 - mmseg - INFO - Iter [12750/80000] lr: 7.500e-05, eta: 5:09:49, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2300, decode.acc_seg: 91.1127, loss: 0.2300 2023-03-03 16:20:57,005 - mmseg - INFO - Iter [12800/80000] lr: 7.500e-05, eta: 5:09:14, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2350, decode.acc_seg: 90.7984, loss: 0.2350 2023-03-03 16:21:06,922 - mmseg - INFO - Iter [12850/80000] lr: 7.500e-05, eta: 5:08:40, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2281, decode.acc_seg: 91.1990, loss: 0.2281 2023-03-03 16:21:17,090 - mmseg - INFO - Iter [12900/80000] lr: 7.500e-05, eta: 5:08:07, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2325, decode.acc_seg: 90.9743, loss: 0.2325 2023-03-03 16:21:27,076 - mmseg - INFO - Iter [12950/80000] lr: 7.500e-05, eta: 5:07:34, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2291, decode.acc_seg: 90.7961, loss: 0.2291 2023-03-03 16:21:37,138 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:21:37,138 - mmseg - INFO - Iter [13000/80000] lr: 7.500e-05, eta: 5:07:01, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2252, decode.acc_seg: 91.1156, loss: 0.2252 2023-03-03 16:21:47,055 - mmseg - INFO - Iter [13050/80000] lr: 7.500e-05, eta: 5:06:28, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2269, decode.acc_seg: 90.9938, loss: 0.2269 2023-03-03 16:21:57,045 - mmseg - INFO - Iter [13100/80000] lr: 7.500e-05, eta: 5:05:55, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2349, decode.acc_seg: 90.7314, loss: 0.2349 2023-03-03 16:22:07,064 - mmseg - INFO - Iter [13150/80000] lr: 7.500e-05, eta: 5:05:22, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2327, decode.acc_seg: 90.9470, loss: 0.2327 2023-03-03 16:22:17,065 - mmseg - INFO - Iter [13200/80000] lr: 7.500e-05, eta: 5:04:50, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2234, decode.acc_seg: 91.2692, loss: 0.2234 2023-03-03 16:22:26,985 - mmseg - INFO - Iter [13250/80000] lr: 7.500e-05, eta: 5:04:17, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2295, decode.acc_seg: 90.8350, loss: 0.2295 2023-03-03 16:22:39,530 - mmseg - INFO - Iter [13300/80000] lr: 7.500e-05, eta: 5:03:58, time: 0.251, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2249, decode.acc_seg: 91.1004, loss: 0.2249 2023-03-03 16:22:49,633 - mmseg - INFO - Iter [13350/80000] lr: 7.500e-05, eta: 5:03:26, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2322, decode.acc_seg: 90.7559, loss: 0.2322 2023-03-03 16:22:59,823 - mmseg - INFO - Iter [13400/80000] lr: 7.500e-05, eta: 5:02:56, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2270, decode.acc_seg: 90.9478, loss: 0.2270 2023-03-03 16:23:10,108 - mmseg - INFO - Iter [13450/80000] lr: 7.500e-05, eta: 5:02:25, time: 0.206, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2281, decode.acc_seg: 90.8048, loss: 0.2281 2023-03-03 16:23:20,119 - mmseg - INFO - Iter [13500/80000] lr: 7.500e-05, eta: 5:01:54, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2258, decode.acc_seg: 91.1792, loss: 0.2258 2023-03-03 16:23:30,127 - mmseg - INFO - Iter [13550/80000] lr: 7.500e-05, eta: 5:01:22, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2220, decode.acc_seg: 91.2745, loss: 0.2220 2023-03-03 16:23:40,235 - mmseg - INFO - Iter [13600/80000] lr: 7.500e-05, eta: 5:00:52, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2381, decode.acc_seg: 90.5109, loss: 0.2381 2023-03-03 16:23:50,223 - mmseg - INFO - Iter [13650/80000] lr: 7.500e-05, eta: 5:00:21, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2287, decode.acc_seg: 91.0088, loss: 0.2287 2023-03-03 16:24:00,349 - mmseg - INFO - Iter [13700/80000] lr: 7.500e-05, eta: 4:59:50, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2324, decode.acc_seg: 90.8933, loss: 0.2324 2023-03-03 16:24:10,390 - mmseg - INFO - Iter [13750/80000] lr: 7.500e-05, eta: 4:59:20, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2310, decode.acc_seg: 90.8504, loss: 0.2310 2023-03-03 16:24:20,454 - mmseg - INFO - Iter [13800/80000] lr: 7.500e-05, eta: 4:58:50, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2327, decode.acc_seg: 90.6689, loss: 0.2327 2023-03-03 16:24:30,632 - mmseg - INFO - Iter [13850/80000] lr: 7.500e-05, eta: 4:58:20, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2267, decode.acc_seg: 91.0670, loss: 0.2267 2023-03-03 16:24:43,355 - mmseg - INFO - Iter [13900/80000] lr: 7.500e-05, eta: 4:58:03, time: 0.254, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2273, decode.acc_seg: 90.9820, loss: 0.2273 2023-03-03 16:24:53,311 - mmseg - INFO - Iter [13950/80000] lr: 7.500e-05, eta: 4:57:32, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2262, decode.acc_seg: 91.1620, loss: 0.2262 2023-03-03 16:25:03,290 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:25:03,290 - mmseg - INFO - Iter [14000/80000] lr: 7.500e-05, eta: 4:57:02, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2227, decode.acc_seg: 91.2747, loss: 0.2227 2023-03-03 16:25:13,463 - mmseg - INFO - Iter [14050/80000] lr: 7.500e-05, eta: 4:56:33, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2280, decode.acc_seg: 90.8473, loss: 0.2280 2023-03-03 16:25:23,517 - mmseg - INFO - Iter [14100/80000] lr: 7.500e-05, eta: 4:56:03, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2281, decode.acc_seg: 90.8802, loss: 0.2281 2023-03-03 16:25:33,602 - mmseg - INFO - Iter [14150/80000] lr: 7.500e-05, eta: 4:55:34, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2248, decode.acc_seg: 90.9930, loss: 0.2248 2023-03-03 16:25:43,559 - mmseg - INFO - Iter [14200/80000] lr: 7.500e-05, eta: 4:55:04, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2287, decode.acc_seg: 90.9138, loss: 0.2287 2023-03-03 16:25:53,687 - mmseg - INFO - Iter [14250/80000] lr: 7.500e-05, eta: 4:54:35, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2236, decode.acc_seg: 91.0788, loss: 0.2236 2023-03-03 16:26:03,544 - mmseg - INFO - Iter [14300/80000] lr: 7.500e-05, eta: 4:54:06, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2363, decode.acc_seg: 90.5300, loss: 0.2363 2023-03-03 16:26:13,643 - mmseg - INFO - Iter [14350/80000] lr: 7.500e-05, eta: 4:53:37, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2352, decode.acc_seg: 90.5310, loss: 0.2352 2023-03-03 16:26:23,647 - mmseg - INFO - Iter [14400/80000] lr: 7.500e-05, eta: 4:53:08, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2287, decode.acc_seg: 91.0200, loss: 0.2287 2023-03-03 16:26:33,823 - mmseg - INFO - Iter [14450/80000] lr: 7.500e-05, eta: 4:52:40, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2356, decode.acc_seg: 90.7865, loss: 0.2356 2023-03-03 16:26:43,934 - mmseg - INFO - Iter [14500/80000] lr: 7.500e-05, eta: 4:52:12, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2195, decode.acc_seg: 91.2570, loss: 0.2195 2023-03-03 16:26:56,372 - mmseg - INFO - Iter [14550/80000] lr: 7.500e-05, eta: 4:51:54, time: 0.249, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2267, decode.acc_seg: 91.0589, loss: 0.2267 2023-03-03 16:27:06,420 - mmseg - INFO - Iter [14600/80000] lr: 7.500e-05, eta: 4:51:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2328, decode.acc_seg: 90.9689, loss: 0.2328 2023-03-03 16:27:16,394 - mmseg - INFO - Iter [14650/80000] lr: 7.500e-05, eta: 4:50:57, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.2595, loss: 0.2211 2023-03-03 16:27:26,548 - mmseg - INFO - Iter [14700/80000] lr: 7.500e-05, eta: 4:50:30, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2245, decode.acc_seg: 90.9278, loss: 0.2245 2023-03-03 16:27:36,494 - mmseg - INFO - Iter [14750/80000] lr: 7.500e-05, eta: 4:50:01, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2242, decode.acc_seg: 91.1285, loss: 0.2242 2023-03-03 16:27:46,609 - mmseg - INFO - Iter [14800/80000] lr: 7.500e-05, eta: 4:49:34, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2290, decode.acc_seg: 90.8519, loss: 0.2290 2023-03-03 16:27:56,771 - mmseg - INFO - Iter [14850/80000] lr: 7.500e-05, eta: 4:49:06, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2263, decode.acc_seg: 91.0469, loss: 0.2263 2023-03-03 16:28:06,986 - mmseg - INFO - Iter [14900/80000] lr: 7.500e-05, eta: 4:48:40, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2293, decode.acc_seg: 91.0546, loss: 0.2293 2023-03-03 16:28:17,102 - mmseg - INFO - Iter [14950/80000] lr: 7.500e-05, eta: 4:48:12, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2309, decode.acc_seg: 90.8957, loss: 0.2309 2023-03-03 16:28:27,168 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:28:27,169 - mmseg - INFO - Iter [15000/80000] lr: 7.500e-05, eta: 4:47:45, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2318, decode.acc_seg: 90.8603, loss: 0.2318 2023-03-03 16:28:37,395 - mmseg - INFO - Iter [15050/80000] lr: 7.500e-05, eta: 4:47:19, time: 0.205, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2269, decode.acc_seg: 90.9094, loss: 0.2269 2023-03-03 16:28:47,314 - mmseg - INFO - Iter [15100/80000] lr: 7.500e-05, eta: 4:46:51, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2202, decode.acc_seg: 91.1271, loss: 0.2202 2023-03-03 16:28:59,910 - mmseg - INFO - Iter [15150/80000] lr: 7.500e-05, eta: 4:46:35, time: 0.252, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2239, decode.acc_seg: 91.0546, loss: 0.2239 2023-03-03 16:29:10,092 - mmseg - INFO - Iter [15200/80000] lr: 7.500e-05, eta: 4:46:09, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2243, decode.acc_seg: 91.0991, loss: 0.2243 2023-03-03 16:29:20,105 - mmseg - INFO - Iter [15250/80000] lr: 7.500e-05, eta: 4:45:42, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2199, decode.acc_seg: 91.2263, loss: 0.2199 2023-03-03 16:29:30,129 - mmseg - INFO - Iter [15300/80000] lr: 7.500e-05, eta: 4:45:15, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2360, decode.acc_seg: 90.6552, loss: 0.2360 2023-03-03 16:29:40,158 - mmseg - INFO - Iter [15350/80000] lr: 7.500e-05, eta: 4:44:48, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.2082, loss: 0.2212 2023-03-03 16:29:50,173 - mmseg - INFO - Iter [15400/80000] lr: 7.500e-05, eta: 4:44:21, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2242, decode.acc_seg: 91.1470, loss: 0.2242 2023-03-03 16:30:00,263 - mmseg - INFO - Iter [15450/80000] lr: 7.500e-05, eta: 4:43:55, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2239, decode.acc_seg: 91.1006, loss: 0.2239 2023-03-03 16:30:10,142 - mmseg - INFO - Iter [15500/80000] lr: 7.500e-05, eta: 4:43:28, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2259, decode.acc_seg: 90.9954, loss: 0.2259 2023-03-03 16:30:20,225 - mmseg - INFO - Iter [15550/80000] lr: 7.500e-05, eta: 4:43:02, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2234, decode.acc_seg: 91.0266, loss: 0.2234 2023-03-03 16:30:30,159 - mmseg - INFO - Iter [15600/80000] lr: 7.500e-05, eta: 4:42:36, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2207, decode.acc_seg: 91.1784, loss: 0.2207 2023-03-03 16:30:40,203 - mmseg - INFO - Iter [15650/80000] lr: 7.500e-05, eta: 4:42:10, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2294, decode.acc_seg: 90.8773, loss: 0.2294 2023-03-03 16:30:50,283 - mmseg - INFO - Iter [15700/80000] lr: 7.500e-05, eta: 4:41:44, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2299, decode.acc_seg: 90.7494, loss: 0.2299 2023-03-03 16:31:00,458 - mmseg - INFO - Iter [15750/80000] lr: 7.500e-05, eta: 4:41:19, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2243, decode.acc_seg: 91.2913, loss: 0.2243 2023-03-03 16:31:13,024 - mmseg - INFO - Iter [15800/80000] lr: 7.500e-05, eta: 4:41:03, time: 0.251, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2317, decode.acc_seg: 90.7957, loss: 0.2317 2023-03-03 16:31:23,089 - mmseg - INFO - Iter [15850/80000] lr: 7.500e-05, eta: 4:40:38, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2238, decode.acc_seg: 91.1359, loss: 0.2238 2023-03-03 16:31:33,167 - mmseg - INFO - Iter [15900/80000] lr: 7.500e-05, eta: 4:40:12, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2373, decode.acc_seg: 90.7941, loss: 0.2373 2023-03-03 16:31:43,296 - mmseg - INFO - Iter [15950/80000] lr: 7.500e-05, eta: 4:39:47, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2204, decode.acc_seg: 91.2396, loss: 0.2204 2023-03-03 16:31:53,303 - mmseg - INFO - Saving checkpoint at 16000 iterations 2023-03-03 16:31:54,311 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:31:54,311 - mmseg - INFO - Iter [16000/80000] lr: 7.500e-05, eta: 4:39:26, time: 0.220, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2284, decode.acc_seg: 90.8249, loss: 0.2284 2023-03-03 16:32:08,866 - mmseg - INFO - per class results: 2023-03-03 16:32:08,876 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 74.65 | 89.26 | | building | 81.68 | 93.91 | | sky | 93.96 | 97.53 | | floor | 78.78 | 89.13 | | tree | 71.95 | 86.39 | | ceiling | 82.07 | 89.56 | | road | 79.88 | 86.66 | | bed | 87.29 | 94.97 | | windowpane | 59.05 | 77.41 | | grass | 64.8 | 82.84 | | cabinet | 57.17 | 70.04 | | sidewalk | 62.99 | 81.41 | | person | 77.32 | 90.26 | | earth | 31.15 | 43.56 | | door | 44.68 | 63.01 | | table | 58.92 | 75.46 | | mountain | 51.31 | 65.3 | | plant | 49.82 | 63.38 | | curtain | 70.67 | 82.03 | | chair | 54.82 | 71.08 | | car | 81.2 | 89.13 | | water | 44.38 | 60.0 | | painting | 71.06 | 83.04 | | sofa | 61.14 | 82.02 | | shelf | 38.41 | 54.74 | | house | 40.02 | 45.16 | | sea | 42.24 | 68.41 | | mirror | 62.24 | 70.42 | | rug | 54.7 | 63.57 | | field | 21.46 | 32.55 | | armchair | 39.24 | 50.62 | | seat | 58.19 | 74.24 | | fence | 31.97 | 41.32 | | desk | 47.98 | 63.08 | | rock | 31.43 | 49.09 | | wardrobe | 46.0 | 61.57 | | lamp | 61.94 | 72.71 | | bathtub | 71.13 | 75.32 | | railing | 29.29 | 46.69 | | cushion | 52.59 | 65.8 | | base | 16.96 | 20.61 | | box | 20.59 | 25.53 | | column | 43.49 | 51.75 | | signboard | 34.43 | 46.13 | | chest of drawers | 37.08 | 51.72 | | counter | 28.2 | 32.94 | | sand | 30.94 | 47.57 | | sink | 66.22 | 76.29 | | skyscraper | 62.25 | 71.23 | | fireplace | 70.4 | 82.96 | | refrigerator | 68.75 | 83.23 | | grandstand | 40.03 | 63.44 | | path | 14.39 | 20.95 | | stairs | 29.24 | 37.39 | | runway | 56.28 | 72.94 | | case | 44.04 | 67.65 | | pool table | 91.25 | 94.93 | | pillow | 51.91 | 63.2 | | screen door | 64.28 | 71.37 | | stairway | 28.99 | 39.24 | | river | 11.84 | 23.5 | | bridge | 60.0 | 64.98 | | bookcase | 35.83 | 43.12 | | blind | 39.64 | 44.96 | | coffee table | 55.46 | 78.45 | | toilet | 85.06 | 89.9 | | flower | 33.96 | 44.14 | | book | 44.85 | 65.09 | | hill | 4.94 | 6.96 | | bench | 38.19 | 46.94 | | countertop | 52.31 | 61.51 | | stove | 71.11 | 80.65 | | palm | 49.59 | 64.74 | | kitchen island | 43.74 | 71.12 | | computer | 53.91 | 60.94 | | swivel chair | 45.61 | 65.69 | | boat | 41.53 | 48.25 | | bar | 23.55 | 26.18 | | arcade machine | 24.74 | 26.54 | | hovel | 30.32 | 31.12 | | bus | 78.24 | 85.95 | | towel | 54.62 | 63.72 | | light | 54.19 | 63.05 | | truck | 28.49 | 37.13 | | tower | 34.83 | 48.51 | | chandelier | 66.39 | 78.79 | | awning | 20.56 | 22.04 | | streetlight | 22.55 | 26.25 | | booth | 37.8 | 39.46 | | television receiver | 67.06 | 78.5 | | airplane | 52.97 | 65.74 | | dirt track | 3.26 | 9.43 | | apparel | 27.55 | 35.01 | | pole | 24.96 | 37.58 | | land | 0.72 | 1.0 | | bannister | 4.8 | 5.69 | | escalator | 21.82 | 22.66 | | ottoman | 41.95 | 54.08 | | bottle | 13.97 | 24.66 | | buffet | 35.53 | 45.69 | | poster | 24.17 | 36.26 | | stage | 7.18 | 8.21 | | van | 40.17 | 54.12 | | ship | 53.26 | 66.68 | | fountain | 0.52 | 0.52 | | conveyer belt | 72.27 | 82.07 | | canopy | 12.39 | 13.08 | | washer | 62.62 | 63.3 | | plaything | 17.75 | 20.0 | | swimming pool | 29.04 | 35.25 | | stool | 37.93 | 47.79 | | barrel | 45.05 | 64.9 | | basket | 24.0 | 32.57 | | waterfall | 61.78 | 83.2 | | tent | 94.11 | 98.1 | | bag | 7.82 | 9.1 | | minibike | 49.24 | 56.74 | | cradle | 75.42 | 98.09 | | oven | 19.84 | 46.36 | | ball | 45.94 | 66.39 | | food | 53.25 | 62.43 | | step | 4.6 | 5.14 | | tank | 42.14 | 42.64 | | trade name | 22.71 | 26.17 | | microwave | 37.75 | 40.34 | | pot | 36.03 | 40.58 | | animal | 51.63 | 53.83 | | bicycle | 44.68 | 71.74 | | lake | 60.29 | 63.08 | | dishwasher | 68.65 | 70.48 | | screen | 59.81 | 68.61 | | blanket | 5.87 | 7.07 | | sculpture | 43.07 | 54.96 | | hood | 60.96 | 65.63 | | sconce | 42.64 | 51.69 | | vase | 32.19 | 47.58 | | traffic light | 21.85 | 27.55 | | tray | 4.13 | 6.16 | | ashcan | 38.94 | 51.13 | | fan | 56.45 | 71.34 | | pier | 20.62 | 26.26 | | crt screen | 3.74 | 6.15 | | plate | 36.71 | 50.93 | | monitor | 54.77 | 61.08 | | bulletin board | 31.64 | 47.05 | | shower | 0.0 | 0.0 | | radiator | 40.69 | 52.09 | | glass | 10.77 | 12.51 | | clock | 16.83 | 20.24 | | flag | 39.21 | 41.66 | +---------------------+-------+-------+ 2023-03-03 16:32:08,876 - mmseg - INFO - Summary: 2023-03-03 16:32:08,877 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.06 | 43.87 | 53.88 | +-------+-------+-------+ 2023-03-03 16:32:08,907 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_8000.pth was removed 2023-03-03 16:32:09,774 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_16000.pth. 2023-03-03 16:32:09,774 - mmseg - INFO - Best mIoU is 0.4387 at 16000 iter. 2023-03-03 16:32:09,774 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:32:09,774 - mmseg - INFO - Iter(val) [250] aAcc: 0.8106, mIoU: 0.4387, mAcc: 0.5388, IoU.background: nan, IoU.wall: 0.7465, IoU.building: 0.8168, IoU.sky: 0.9396, IoU.floor: 0.7878, IoU.tree: 0.7195, IoU.ceiling: 0.8207, IoU.road: 0.7988, IoU.bed : 0.8729, IoU.windowpane: 0.5905, IoU.grass: 0.6480, IoU.cabinet: 0.5717, IoU.sidewalk: 0.6299, IoU.person: 0.7732, IoU.earth: 0.3115, IoU.door: 0.4468, IoU.table: 0.5892, IoU.mountain: 0.5131, IoU.plant: 0.4982, IoU.curtain: 0.7067, IoU.chair: 0.5482, IoU.car: 0.8120, IoU.water: 0.4438, IoU.painting: 0.7106, IoU.sofa: 0.6114, IoU.shelf: 0.3841, IoU.house: 0.4002, IoU.sea: 0.4224, IoU.mirror: 0.6224, IoU.rug: 0.5470, IoU.field: 0.2146, IoU.armchair: 0.3924, IoU.seat: 0.5819, IoU.fence: 0.3197, IoU.desk: 0.4798, IoU.rock: 0.3143, IoU.wardrobe: 0.4600, IoU.lamp: 0.6194, IoU.bathtub: 0.7113, IoU.railing: 0.2929, IoU.cushion: 0.5259, IoU.base: 0.1696, IoU.box: 0.2059, IoU.column: 0.4349, IoU.signboard: 0.3443, IoU.chest of drawers: 0.3708, IoU.counter: 0.2820, IoU.sand: 0.3094, IoU.sink: 0.6622, IoU.skyscraper: 0.6225, IoU.fireplace: 0.7040, IoU.refrigerator: 0.6875, IoU.grandstand: 0.4003, IoU.path: 0.1439, IoU.stairs: 0.2924, IoU.runway: 0.5628, IoU.case: 0.4404, IoU.pool table: 0.9125, IoU.pillow: 0.5191, IoU.screen door: 0.6428, IoU.stairway: 0.2899, IoU.river: 0.1184, IoU.bridge: 0.6000, IoU.bookcase: 0.3583, IoU.blind: 0.3964, IoU.coffee table: 0.5546, IoU.toilet: 0.8506, IoU.flower: 0.3396, IoU.book: 0.4485, IoU.hill: 0.0494, IoU.bench: 0.3819, IoU.countertop: 0.5231, IoU.stove: 0.7111, IoU.palm: 0.4959, IoU.kitchen island: 0.4374, IoU.computer: 0.5391, IoU.swivel chair: 0.4561, IoU.boat: 0.4153, IoU.bar: 0.2355, IoU.arcade machine: 0.2474, IoU.hovel: 0.3032, IoU.bus: 0.7824, IoU.towel: 0.5462, IoU.light: 0.5419, IoU.truck: 0.2849, IoU.tower: 0.3483, IoU.chandelier: 0.6639, IoU.awning: 0.2056, IoU.streetlight: 0.2255, IoU.booth: 0.3780, IoU.television receiver: 0.6706, IoU.airplane: 0.5297, IoU.dirt track: 0.0326, IoU.apparel: 0.2755, IoU.pole: 0.2496, IoU.land: 0.0072, IoU.bannister: 0.0480, IoU.escalator: 0.2182, IoU.ottoman: 0.4195, IoU.bottle: 0.1397, IoU.buffet: 0.3553, IoU.poster: 0.2417, IoU.stage: 0.0718, IoU.van: 0.4017, IoU.ship: 0.5326, IoU.fountain: 0.0052, IoU.conveyer belt: 0.7227, IoU.canopy: 0.1239, IoU.washer: 0.6262, IoU.plaything: 0.1775, IoU.swimming pool: 0.2904, IoU.stool: 0.3793, IoU.barrel: 0.4505, IoU.basket: 0.2400, IoU.waterfall: 0.6178, IoU.tent: 0.9411, IoU.bag: 0.0782, IoU.minibike: 0.4924, IoU.cradle: 0.7542, IoU.oven: 0.1984, IoU.ball: 0.4594, IoU.food: 0.5325, IoU.step: 0.0460, IoU.tank: 0.4214, IoU.trade name: 0.2271, IoU.microwave: 0.3775, IoU.pot: 0.3603, IoU.animal: 0.5163, IoU.bicycle: 0.4468, IoU.lake: 0.6029, IoU.dishwasher: 0.6865, IoU.screen: 0.5981, IoU.blanket: 0.0587, IoU.sculpture: 0.4307, IoU.hood: 0.6096, IoU.sconce: 0.4264, IoU.vase: 0.3219, IoU.traffic light: 0.2185, IoU.tray: 0.0413, IoU.ashcan: 0.3894, IoU.fan: 0.5645, IoU.pier: 0.2062, IoU.crt screen: 0.0374, IoU.plate: 0.3671, IoU.monitor: 0.5477, IoU.bulletin board: 0.3164, IoU.shower: 0.0000, IoU.radiator: 0.4069, IoU.glass: 0.1077, IoU.clock: 0.1683, IoU.flag: 0.3921, Acc.background: nan, Acc.wall: 0.8926, Acc.building: 0.9391, Acc.sky: 0.9753, Acc.floor: 0.8913, Acc.tree: 0.8639, Acc.ceiling: 0.8956, Acc.road: 0.8666, Acc.bed : 0.9497, Acc.windowpane: 0.7741, Acc.grass: 0.8284, Acc.cabinet: 0.7004, Acc.sidewalk: 0.8141, Acc.person: 0.9026, Acc.earth: 0.4356, Acc.door: 0.6301, Acc.table: 0.7546, Acc.mountain: 0.6530, Acc.plant: 0.6338, Acc.curtain: 0.8203, Acc.chair: 0.7108, Acc.car: 0.8913, Acc.water: 0.6000, Acc.painting: 0.8304, Acc.sofa: 0.8202, Acc.shelf: 0.5474, Acc.house: 0.4516, Acc.sea: 0.6841, Acc.mirror: 0.7042, Acc.rug: 0.6357, Acc.field: 0.3255, Acc.armchair: 0.5062, Acc.seat: 0.7424, Acc.fence: 0.4132, Acc.desk: 0.6308, Acc.rock: 0.4909, Acc.wardrobe: 0.6157, Acc.lamp: 0.7271, Acc.bathtub: 0.7532, Acc.railing: 0.4669, Acc.cushion: 0.6580, Acc.base: 0.2061, Acc.box: 0.2553, Acc.column: 0.5175, Acc.signboard: 0.4613, Acc.chest of drawers: 0.5172, Acc.counter: 0.3294, Acc.sand: 0.4757, Acc.sink: 0.7629, Acc.skyscraper: 0.7123, Acc.fireplace: 0.8296, Acc.refrigerator: 0.8323, Acc.grandstand: 0.6344, Acc.path: 0.2095, Acc.stairs: 0.3739, Acc.runway: 0.7294, Acc.case: 0.6765, Acc.pool table: 0.9493, Acc.pillow: 0.6320, Acc.screen door: 0.7137, Acc.stairway: 0.3924, Acc.river: 0.2350, Acc.bridge: 0.6498, Acc.bookcase: 0.4312, Acc.blind: 0.4496, Acc.coffee table: 0.7845, Acc.toilet: 0.8990, Acc.flower: 0.4414, Acc.book: 0.6509, Acc.hill: 0.0696, Acc.bench: 0.4694, Acc.countertop: 0.6151, Acc.stove: 0.8065, Acc.palm: 0.6474, Acc.kitchen island: 0.7112, Acc.computer: 0.6094, Acc.swivel chair: 0.6569, Acc.boat: 0.4825, Acc.bar: 0.2618, Acc.arcade machine: 0.2654, Acc.hovel: 0.3112, Acc.bus: 0.8595, Acc.towel: 0.6372, Acc.light: 0.6305, Acc.truck: 0.3713, Acc.tower: 0.4851, Acc.chandelier: 0.7879, Acc.awning: 0.2204, Acc.streetlight: 0.2625, Acc.booth: 0.3946, Acc.television receiver: 0.7850, Acc.airplane: 0.6574, Acc.dirt track: 0.0943, Acc.apparel: 0.3501, Acc.pole: 0.3758, Acc.land: 0.0100, Acc.bannister: 0.0569, Acc.escalator: 0.2266, Acc.ottoman: 0.5408, Acc.bottle: 0.2466, Acc.buffet: 0.4569, Acc.poster: 0.3626, Acc.stage: 0.0821, Acc.van: 0.5412, Acc.ship: 0.6668, Acc.fountain: 0.0052, Acc.conveyer belt: 0.8207, Acc.canopy: 0.1308, Acc.washer: 0.6330, Acc.plaything: 0.2000, Acc.swimming pool: 0.3525, Acc.stool: 0.4779, Acc.barrel: 0.6490, Acc.basket: 0.3257, Acc.waterfall: 0.8320, Acc.tent: 0.9810, Acc.bag: 0.0910, Acc.minibike: 0.5674, Acc.cradle: 0.9809, Acc.oven: 0.4636, Acc.ball: 0.6639, Acc.food: 0.6243, Acc.step: 0.0514, Acc.tank: 0.4264, Acc.trade name: 0.2617, Acc.microwave: 0.4034, Acc.pot: 0.4058, Acc.animal: 0.5383, Acc.bicycle: 0.7174, Acc.lake: 0.6308, Acc.dishwasher: 0.7048, Acc.screen: 0.6861, Acc.blanket: 0.0707, Acc.sculpture: 0.5496, Acc.hood: 0.6563, Acc.sconce: 0.5169, Acc.vase: 0.4758, Acc.traffic light: 0.2755, Acc.tray: 0.0616, Acc.ashcan: 0.5113, Acc.fan: 0.7134, Acc.pier: 0.2626, Acc.crt screen: 0.0615, Acc.plate: 0.5093, Acc.monitor: 0.6108, Acc.bulletin board: 0.4705, Acc.shower: 0.0000, Acc.radiator: 0.5209, Acc.glass: 0.1251, Acc.clock: 0.2024, Acc.flag: 0.4166 2023-03-03 16:32:20,426 - mmseg - INFO - Iter [16050/80000] lr: 7.500e-05, eta: 4:40:04, time: 0.522, data_time: 0.317, memory: 67202, decode.loss_ce: 0.2262, decode.acc_seg: 91.0391, loss: 0.2262 2023-03-03 16:32:30,761 - mmseg - INFO - Iter [16100/80000] lr: 7.500e-05, eta: 4:39:40, time: 0.207, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2251, decode.acc_seg: 91.1114, loss: 0.2251 2023-03-03 16:32:40,905 - mmseg - INFO - Iter [16150/80000] lr: 7.500e-05, eta: 4:39:15, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2292, decode.acc_seg: 90.9274, loss: 0.2292 2023-03-03 16:32:50,954 - mmseg - INFO - Iter [16200/80000] lr: 7.500e-05, eta: 4:38:50, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2300, decode.acc_seg: 90.8340, loss: 0.2300 2023-03-03 16:33:00,942 - mmseg - INFO - Iter [16250/80000] lr: 7.500e-05, eta: 4:38:25, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2220, decode.acc_seg: 91.1600, loss: 0.2220 2023-03-03 16:33:11,209 - mmseg - INFO - Iter [16300/80000] lr: 7.500e-05, eta: 4:38:00, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2259, decode.acc_seg: 91.1447, loss: 0.2259 2023-03-03 16:33:21,426 - mmseg - INFO - Iter [16350/80000] lr: 7.500e-05, eta: 4:37:36, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2248, decode.acc_seg: 91.1521, loss: 0.2248 2023-03-03 16:33:31,447 - mmseg - INFO - Iter [16400/80000] lr: 7.500e-05, eta: 4:37:11, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2213, decode.acc_seg: 91.2052, loss: 0.2213 2023-03-03 16:33:43,898 - mmseg - INFO - Iter [16450/80000] lr: 7.500e-05, eta: 4:36:56, time: 0.249, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2256, decode.acc_seg: 90.9830, loss: 0.2256 2023-03-03 16:33:53,968 - mmseg - INFO - Iter [16500/80000] lr: 7.500e-05, eta: 4:36:31, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2292, decode.acc_seg: 90.9883, loss: 0.2292 2023-03-03 16:34:03,994 - mmseg - INFO - Iter [16550/80000] lr: 7.500e-05, eta: 4:36:06, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2213, decode.acc_seg: 91.1096, loss: 0.2213 2023-03-03 16:34:13,988 - mmseg - INFO - Iter [16600/80000] lr: 7.500e-05, eta: 4:35:42, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2227, decode.acc_seg: 91.1694, loss: 0.2227 2023-03-03 16:34:24,227 - mmseg - INFO - Iter [16650/80000] lr: 7.500e-05, eta: 4:35:18, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2234, decode.acc_seg: 91.5371, loss: 0.2234 2023-03-03 16:34:34,217 - mmseg - INFO - Iter [16700/80000] lr: 7.500e-05, eta: 4:34:53, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2239, decode.acc_seg: 91.0259, loss: 0.2239 2023-03-03 16:34:44,166 - mmseg - INFO - Iter [16750/80000] lr: 7.500e-05, eta: 4:34:29, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2345, decode.acc_seg: 90.8710, loss: 0.2345 2023-03-03 16:34:54,299 - mmseg - INFO - Iter [16800/80000] lr: 7.500e-05, eta: 4:34:05, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2312, decode.acc_seg: 90.8311, loss: 0.2312 2023-03-03 16:35:04,282 - mmseg - INFO - Iter [16850/80000] lr: 7.500e-05, eta: 4:33:40, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2263, decode.acc_seg: 90.8743, loss: 0.2263 2023-03-03 16:35:14,349 - mmseg - INFO - Iter [16900/80000] lr: 7.500e-05, eta: 4:33:16, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2254, decode.acc_seg: 91.1192, loss: 0.2254 2023-03-03 16:35:24,254 - mmseg - INFO - Iter [16950/80000] lr: 7.500e-05, eta: 4:32:52, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2168, decode.acc_seg: 91.3658, loss: 0.2168 2023-03-03 16:35:34,235 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:35:34,235 - mmseg - INFO - Iter [17000/80000] lr: 7.500e-05, eta: 4:32:28, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2368, decode.acc_seg: 90.5663, loss: 0.2368 2023-03-03 16:35:46,979 - mmseg - INFO - Iter [17050/80000] lr: 7.500e-05, eta: 4:32:14, time: 0.255, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2250, decode.acc_seg: 91.1436, loss: 0.2250 2023-03-03 16:35:56,888 - mmseg - INFO - Iter [17100/80000] lr: 7.500e-05, eta: 4:31:50, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2231, decode.acc_seg: 91.1035, loss: 0.2231 2023-03-03 16:36:06,915 - mmseg - INFO - Iter [17150/80000] lr: 7.500e-05, eta: 4:31:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2267, decode.acc_seg: 91.1915, loss: 0.2267 2023-03-03 16:36:17,056 - mmseg - INFO - Iter [17200/80000] lr: 7.500e-05, eta: 4:31:03, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2221, decode.acc_seg: 91.2760, loss: 0.2221 2023-03-03 16:36:27,084 - mmseg - INFO - Iter [17250/80000] lr: 7.500e-05, eta: 4:30:39, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2230, decode.acc_seg: 90.9837, loss: 0.2230 2023-03-03 16:36:37,359 - mmseg - INFO - Iter [17300/80000] lr: 7.500e-05, eta: 4:30:17, time: 0.205, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2317, decode.acc_seg: 90.9455, loss: 0.2317 2023-03-03 16:36:47,337 - mmseg - INFO - Iter [17350/80000] lr: 7.500e-05, eta: 4:29:53, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2297, decode.acc_seg: 90.8789, loss: 0.2297 2023-03-03 16:36:57,403 - mmseg - INFO - Iter [17400/80000] lr: 7.500e-05, eta: 4:29:30, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2221, decode.acc_seg: 91.1621, loss: 0.2221 2023-03-03 16:37:07,361 - mmseg - INFO - Iter [17450/80000] lr: 7.500e-05, eta: 4:29:06, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2271, decode.acc_seg: 91.2847, loss: 0.2271 2023-03-03 16:37:17,425 - mmseg - INFO - Iter [17500/80000] lr: 7.500e-05, eta: 4:28:43, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2352, decode.acc_seg: 90.8718, loss: 0.2352 2023-03-03 16:37:27,417 - mmseg - INFO - Iter [17550/80000] lr: 7.500e-05, eta: 4:28:20, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2255, decode.acc_seg: 91.0892, loss: 0.2255 2023-03-03 16:37:37,405 - mmseg - INFO - Iter [17600/80000] lr: 7.500e-05, eta: 4:27:57, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2257, decode.acc_seg: 90.9114, loss: 0.2257 2023-03-03 16:37:47,524 - mmseg - INFO - Iter [17650/80000] lr: 7.500e-05, eta: 4:27:34, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2229, decode.acc_seg: 91.1180, loss: 0.2229 2023-03-03 16:38:00,224 - mmseg - INFO - Iter [17700/80000] lr: 7.500e-05, eta: 4:27:21, time: 0.254, data_time: 0.058, memory: 67202, decode.loss_ce: 0.2221, decode.acc_seg: 91.1480, loss: 0.2221 2023-03-03 16:38:10,184 - mmseg - INFO - Iter [17750/80000] lr: 7.500e-05, eta: 4:26:58, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2283, decode.acc_seg: 91.0074, loss: 0.2283 2023-03-03 16:38:20,302 - mmseg - INFO - Iter [17800/80000] lr: 7.500e-05, eta: 4:26:35, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2225, decode.acc_seg: 91.1085, loss: 0.2225 2023-03-03 16:38:30,295 - mmseg - INFO - Iter [17850/80000] lr: 7.500e-05, eta: 4:26:12, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2247, decode.acc_seg: 91.2183, loss: 0.2247 2023-03-03 16:38:40,253 - mmseg - INFO - Iter [17900/80000] lr: 7.500e-05, eta: 4:25:49, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.2188, loss: 0.2181 2023-03-03 16:38:50,292 - mmseg - INFO - Iter [17950/80000] lr: 7.500e-05, eta: 4:25:27, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2306, decode.acc_seg: 91.0071, loss: 0.2306 2023-03-03 16:39:00,241 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:39:00,241 - mmseg - INFO - Iter [18000/80000] lr: 7.500e-05, eta: 4:25:04, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2177, decode.acc_seg: 91.3951, loss: 0.2177 2023-03-03 16:39:10,216 - mmseg - INFO - Iter [18050/80000] lr: 7.500e-05, eta: 4:24:41, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2171, decode.acc_seg: 91.2803, loss: 0.2171 2023-03-03 16:39:20,197 - mmseg - INFO - Iter [18100/80000] lr: 7.500e-05, eta: 4:24:19, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.1028, loss: 0.2211 2023-03-03 16:39:30,142 - mmseg - INFO - Iter [18150/80000] lr: 7.500e-05, eta: 4:23:56, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2359, decode.acc_seg: 90.5892, loss: 0.2359 2023-03-03 16:39:40,099 - mmseg - INFO - Iter [18200/80000] lr: 7.500e-05, eta: 4:23:34, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2246, decode.acc_seg: 91.0047, loss: 0.2246 2023-03-03 16:39:50,164 - mmseg - INFO - Iter [18250/80000] lr: 7.500e-05, eta: 4:23:12, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2198, decode.acc_seg: 91.1848, loss: 0.2198 2023-03-03 16:40:02,976 - mmseg - INFO - Iter [18300/80000] lr: 7.500e-05, eta: 4:22:59, time: 0.256, data_time: 0.060, memory: 67202, decode.loss_ce: 0.2258, decode.acc_seg: 91.0959, loss: 0.2258 2023-03-03 16:40:12,883 - mmseg - INFO - Iter [18350/80000] lr: 7.500e-05, eta: 4:22:37, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2355, decode.acc_seg: 90.7927, loss: 0.2355 2023-03-03 16:40:22,858 - mmseg - INFO - Iter [18400/80000] lr: 7.500e-05, eta: 4:22:14, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2256, decode.acc_seg: 91.0703, loss: 0.2256 2023-03-03 16:40:32,866 - mmseg - INFO - Iter [18450/80000] lr: 7.500e-05, eta: 4:21:52, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2268, decode.acc_seg: 90.8826, loss: 0.2268 2023-03-03 16:40:42,873 - mmseg - INFO - Iter [18500/80000] lr: 7.500e-05, eta: 4:21:31, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2265, decode.acc_seg: 91.1488, loss: 0.2265 2023-03-03 16:40:52,975 - mmseg - INFO - Iter [18550/80000] lr: 7.500e-05, eta: 4:21:09, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2305, decode.acc_seg: 90.8963, loss: 0.2305 2023-03-03 16:41:02,930 - mmseg - INFO - Iter [18600/80000] lr: 7.500e-05, eta: 4:20:47, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2294, decode.acc_seg: 90.9074, loss: 0.2294 2023-03-03 16:41:13,005 - mmseg - INFO - Iter [18650/80000] lr: 7.500e-05, eta: 4:20:25, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2275, decode.acc_seg: 90.9939, loss: 0.2275 2023-03-03 16:41:23,023 - mmseg - INFO - Iter [18700/80000] lr: 7.500e-05, eta: 4:20:04, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2155, decode.acc_seg: 91.3583, loss: 0.2155 2023-03-03 16:41:32,954 - mmseg - INFO - Iter [18750/80000] lr: 7.500e-05, eta: 4:19:42, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2295, decode.acc_seg: 90.8120, loss: 0.2295 2023-03-03 16:41:42,878 - mmseg - INFO - Iter [18800/80000] lr: 7.500e-05, eta: 4:19:20, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2205, decode.acc_seg: 91.2412, loss: 0.2205 2023-03-03 16:41:52,915 - mmseg - INFO - Iter [18850/80000] lr: 7.500e-05, eta: 4:18:59, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2223, decode.acc_seg: 91.0310, loss: 0.2223 2023-03-03 16:42:02,981 - mmseg - INFO - Iter [18900/80000] lr: 7.500e-05, eta: 4:18:38, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2293, decode.acc_seg: 91.0236, loss: 0.2293 2023-03-03 16:42:15,445 - mmseg - INFO - Iter [18950/80000] lr: 7.500e-05, eta: 4:18:24, time: 0.249, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2286, decode.acc_seg: 91.1069, loss: 0.2286 2023-03-03 16:42:25,403 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:42:25,403 - mmseg - INFO - Iter [19000/80000] lr: 7.500e-05, eta: 4:18:03, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2344, decode.acc_seg: 90.8311, loss: 0.2344 2023-03-03 16:42:35,630 - mmseg - INFO - Iter [19050/80000] lr: 7.500e-05, eta: 4:17:42, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2341, decode.acc_seg: 90.7901, loss: 0.2341 2023-03-03 16:42:45,548 - mmseg - INFO - Iter [19100/80000] lr: 7.500e-05, eta: 4:17:20, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2258, decode.acc_seg: 91.2447, loss: 0.2258 2023-03-03 16:42:55,456 - mmseg - INFO - Iter [19150/80000] lr: 7.500e-05, eta: 4:16:59, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2267, decode.acc_seg: 91.0294, loss: 0.2267 2023-03-03 16:43:05,509 - mmseg - INFO - Iter [19200/80000] lr: 7.500e-05, eta: 4:16:38, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.2351, loss: 0.2144 2023-03-03 16:43:15,532 - mmseg - INFO - Iter [19250/80000] lr: 7.500e-05, eta: 4:16:17, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2190, decode.acc_seg: 91.2414, loss: 0.2190 2023-03-03 16:43:25,656 - mmseg - INFO - Iter [19300/80000] lr: 7.500e-05, eta: 4:15:56, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2192, decode.acc_seg: 91.1942, loss: 0.2192 2023-03-03 16:43:35,652 - mmseg - INFO - Iter [19350/80000] lr: 7.500e-05, eta: 4:15:35, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2387, decode.acc_seg: 90.5944, loss: 0.2387 2023-03-03 16:43:45,854 - mmseg - INFO - Iter [19400/80000] lr: 7.500e-05, eta: 4:15:15, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2330, decode.acc_seg: 90.7118, loss: 0.2330 2023-03-03 16:43:55,974 - mmseg - INFO - Iter [19450/80000] lr: 7.500e-05, eta: 4:14:55, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2214, decode.acc_seg: 91.1990, loss: 0.2214 2023-03-03 16:44:06,190 - mmseg - INFO - Iter [19500/80000] lr: 7.500e-05, eta: 4:14:35, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2225, decode.acc_seg: 91.0562, loss: 0.2225 2023-03-03 16:44:16,214 - mmseg - INFO - Iter [19550/80000] lr: 7.500e-05, eta: 4:14:14, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2286, decode.acc_seg: 91.0166, loss: 0.2286 2023-03-03 16:44:28,667 - mmseg - INFO - Iter [19600/80000] lr: 7.500e-05, eta: 4:14:01, time: 0.249, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2202, decode.acc_seg: 91.1547, loss: 0.2202 2023-03-03 16:44:38,556 - mmseg - INFO - Iter [19650/80000] lr: 7.500e-05, eta: 4:13:40, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2316, decode.acc_seg: 90.9179, loss: 0.2316 2023-03-03 16:44:48,753 - mmseg - INFO - Iter [19700/80000] lr: 7.500e-05, eta: 4:13:20, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2208, decode.acc_seg: 91.0993, loss: 0.2208 2023-03-03 16:44:58,932 - mmseg - INFO - Iter [19750/80000] lr: 7.500e-05, eta: 4:13:00, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2202, decode.acc_seg: 91.3563, loss: 0.2202 2023-03-03 16:45:09,073 - mmseg - INFO - Iter [19800/80000] lr: 7.500e-05, eta: 4:12:40, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2238, decode.acc_seg: 91.1335, loss: 0.2238 2023-03-03 16:45:19,231 - mmseg - INFO - Iter [19850/80000] lr: 7.500e-05, eta: 4:12:20, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2172, decode.acc_seg: 91.2897, loss: 0.2172 2023-03-03 16:45:29,269 - mmseg - INFO - Iter [19900/80000] lr: 7.500e-05, eta: 4:11:59, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2261, decode.acc_seg: 90.9854, loss: 0.2261 2023-03-03 16:45:39,263 - mmseg - INFO - Iter [19950/80000] lr: 7.500e-05, eta: 4:11:39, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2188, decode.acc_seg: 91.2629, loss: 0.2188 2023-03-03 16:45:49,188 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:45:49,188 - mmseg - INFO - Iter [20000/80000] lr: 7.500e-05, eta: 4:11:19, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2270, decode.acc_seg: 91.0745, loss: 0.2270 2023-03-03 16:45:59,131 - mmseg - INFO - Iter [20050/80000] lr: 3.750e-05, eta: 4:10:58, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2206, decode.acc_seg: 91.2511, loss: 0.2206 2023-03-03 16:46:09,234 - mmseg - INFO - Iter [20100/80000] lr: 3.750e-05, eta: 4:10:38, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.0083, loss: 0.2212 2023-03-03 16:46:19,226 - mmseg - INFO - Iter [20150/80000] lr: 3.750e-05, eta: 4:10:18, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2258, decode.acc_seg: 91.0859, loss: 0.2258 2023-03-03 16:46:31,678 - mmseg - INFO - Iter [20200/80000] lr: 3.750e-05, eta: 4:10:05, time: 0.249, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2267, decode.acc_seg: 91.0305, loss: 0.2267 2023-03-03 16:46:41,661 - mmseg - INFO - Iter [20250/80000] lr: 3.750e-05, eta: 4:09:45, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.3253, loss: 0.2153 2023-03-03 16:46:51,637 - mmseg - INFO - Iter [20300/80000] lr: 3.750e-05, eta: 4:09:25, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2220, decode.acc_seg: 91.1681, loss: 0.2220 2023-03-03 16:47:01,487 - mmseg - INFO - Iter [20350/80000] lr: 3.750e-05, eta: 4:09:05, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2277, decode.acc_seg: 91.0116, loss: 0.2277 2023-03-03 16:47:11,579 - mmseg - INFO - Iter [20400/80000] lr: 3.750e-05, eta: 4:08:45, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2230, decode.acc_seg: 91.2256, loss: 0.2230 2023-03-03 16:47:21,518 - mmseg - INFO - Iter [20450/80000] lr: 3.750e-05, eta: 4:08:25, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2256, decode.acc_seg: 91.0654, loss: 0.2256 2023-03-03 16:47:31,567 - mmseg - INFO - Iter [20500/80000] lr: 3.750e-05, eta: 4:08:05, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2176, decode.acc_seg: 91.2826, loss: 0.2176 2023-03-03 16:47:41,586 - mmseg - INFO - Iter [20550/80000] lr: 3.750e-05, eta: 4:07:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2276, decode.acc_seg: 91.0889, loss: 0.2276 2023-03-03 16:47:51,669 - mmseg - INFO - Iter [20600/80000] lr: 3.750e-05, eta: 4:07:26, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2185, decode.acc_seg: 91.2166, loss: 0.2185 2023-03-03 16:48:01,716 - mmseg - INFO - Iter [20650/80000] lr: 3.750e-05, eta: 4:07:07, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2174, decode.acc_seg: 91.4323, loss: 0.2174 2023-03-03 16:48:11,648 - mmseg - INFO - Iter [20700/80000] lr: 3.750e-05, eta: 4:06:47, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2287, decode.acc_seg: 91.0234, loss: 0.2287 2023-03-03 16:48:21,614 - mmseg - INFO - Iter [20750/80000] lr: 3.750e-05, eta: 4:06:27, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2139, decode.acc_seg: 91.4022, loss: 0.2139 2023-03-03 16:48:31,532 - mmseg - INFO - Iter [20800/80000] lr: 3.750e-05, eta: 4:06:07, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2258, decode.acc_seg: 90.9741, loss: 0.2258 2023-03-03 16:48:43,932 - mmseg - INFO - Iter [20850/80000] lr: 3.750e-05, eta: 4:05:55, time: 0.248, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2194, decode.acc_seg: 91.1271, loss: 0.2194 2023-03-03 16:48:53,961 - mmseg - INFO - Iter [20900/80000] lr: 3.750e-05, eta: 4:05:35, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2203, decode.acc_seg: 91.1451, loss: 0.2203 2023-03-03 16:49:03,888 - mmseg - INFO - Iter [20950/80000] lr: 3.750e-05, eta: 4:05:16, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2234, decode.acc_seg: 91.0845, loss: 0.2234 2023-03-03 16:49:13,929 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:49:13,929 - mmseg - INFO - Iter [21000/80000] lr: 3.750e-05, eta: 4:04:56, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2157, decode.acc_seg: 91.2795, loss: 0.2157 2023-03-03 16:49:23,952 - mmseg - INFO - Iter [21050/80000] lr: 3.750e-05, eta: 4:04:37, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2217, decode.acc_seg: 91.1717, loss: 0.2217 2023-03-03 16:49:33,944 - mmseg - INFO - Iter [21100/80000] lr: 3.750e-05, eta: 4:04:18, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2137, decode.acc_seg: 91.5222, loss: 0.2137 2023-03-03 16:49:43,992 - mmseg - INFO - Iter [21150/80000] lr: 3.750e-05, eta: 4:03:59, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2252, decode.acc_seg: 91.0376, loss: 0.2252 2023-03-03 16:49:53,981 - mmseg - INFO - Iter [21200/80000] lr: 3.750e-05, eta: 4:03:40, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2214, decode.acc_seg: 91.3310, loss: 0.2214 2023-03-03 16:50:03,867 - mmseg - INFO - Iter [21250/80000] lr: 3.750e-05, eta: 4:03:20, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 90.9593, loss: 0.2211 2023-03-03 16:50:13,843 - mmseg - INFO - Iter [21300/80000] lr: 3.750e-05, eta: 4:03:01, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2300, decode.acc_seg: 91.1557, loss: 0.2300 2023-03-03 16:50:23,970 - mmseg - INFO - Iter [21350/80000] lr: 3.750e-05, eta: 4:02:42, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2288, decode.acc_seg: 90.9266, loss: 0.2288 2023-03-03 16:50:33,976 - mmseg - INFO - Iter [21400/80000] lr: 3.750e-05, eta: 4:02:23, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2225, decode.acc_seg: 91.0624, loss: 0.2225 2023-03-03 16:50:43,829 - mmseg - INFO - Iter [21450/80000] lr: 3.750e-05, eta: 4:02:04, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2321, decode.acc_seg: 90.9304, loss: 0.2321 2023-03-03 16:50:56,219 - mmseg - INFO - Iter [21500/80000] lr: 3.750e-05, eta: 4:01:51, time: 0.248, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2188, decode.acc_seg: 91.2829, loss: 0.2188 2023-03-03 16:51:06,128 - mmseg - INFO - Iter [21550/80000] lr: 3.750e-05, eta: 4:01:32, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2218, decode.acc_seg: 91.2255, loss: 0.2218 2023-03-03 16:51:16,212 - mmseg - INFO - Iter [21600/80000] lr: 3.750e-05, eta: 4:01:13, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2222, decode.acc_seg: 91.2158, loss: 0.2222 2023-03-03 16:51:26,067 - mmseg - INFO - Iter [21650/80000] lr: 3.750e-05, eta: 4:00:54, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2158, decode.acc_seg: 91.3149, loss: 0.2158 2023-03-03 16:51:36,193 - mmseg - INFO - Iter [21700/80000] lr: 3.750e-05, eta: 4:00:36, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2264, decode.acc_seg: 90.9226, loss: 0.2264 2023-03-03 16:51:46,128 - mmseg - INFO - Iter [21750/80000] lr: 3.750e-05, eta: 4:00:17, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2187, decode.acc_seg: 91.2905, loss: 0.2187 2023-03-03 16:51:56,257 - mmseg - INFO - Iter [21800/80000] lr: 3.750e-05, eta: 3:59:58, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2325, decode.acc_seg: 90.8337, loss: 0.2325 2023-03-03 16:52:06,334 - mmseg - INFO - Iter [21850/80000] lr: 3.750e-05, eta: 3:59:40, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.4326, loss: 0.2153 2023-03-03 16:52:16,643 - mmseg - INFO - Iter [21900/80000] lr: 3.750e-05, eta: 3:59:22, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2111, decode.acc_seg: 91.3728, loss: 0.2111 2023-03-03 16:52:26,527 - mmseg - INFO - Iter [21950/80000] lr: 3.750e-05, eta: 3:59:03, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2198, decode.acc_seg: 91.3930, loss: 0.2198 2023-03-03 16:52:36,562 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:52:36,563 - mmseg - INFO - Iter [22000/80000] lr: 3.750e-05, eta: 3:58:45, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2175, decode.acc_seg: 91.2184, loss: 0.2175 2023-03-03 16:52:46,543 - mmseg - INFO - Iter [22050/80000] lr: 3.750e-05, eta: 3:58:26, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.6529, loss: 0.2092 2023-03-03 16:52:59,002 - mmseg - INFO - Iter [22100/80000] lr: 3.750e-05, eta: 3:58:14, time: 0.249, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2251, decode.acc_seg: 91.1710, loss: 0.2251 2023-03-03 16:53:08,902 - mmseg - INFO - Iter [22150/80000] lr: 3.750e-05, eta: 3:57:55, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2225, decode.acc_seg: 91.2699, loss: 0.2225 2023-03-03 16:53:18,907 - mmseg - INFO - Iter [22200/80000] lr: 3.750e-05, eta: 3:57:37, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2166, decode.acc_seg: 91.3704, loss: 0.2166 2023-03-03 16:53:28,939 - mmseg - INFO - Iter [22250/80000] lr: 3.750e-05, eta: 3:57:19, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2203, decode.acc_seg: 91.2687, loss: 0.2203 2023-03-03 16:53:38,959 - mmseg - INFO - Iter [22300/80000] lr: 3.750e-05, eta: 3:57:00, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.3751, loss: 0.2159 2023-03-03 16:53:48,940 - mmseg - INFO - Iter [22350/80000] lr: 3.750e-05, eta: 3:56:42, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2200, decode.acc_seg: 91.1224, loss: 0.2200 2023-03-03 16:53:59,082 - mmseg - INFO - Iter [22400/80000] lr: 3.750e-05, eta: 3:56:24, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2284, decode.acc_seg: 91.0413, loss: 0.2284 2023-03-03 16:54:09,086 - mmseg - INFO - Iter [22450/80000] lr: 3.750e-05, eta: 3:56:06, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2277, decode.acc_seg: 90.9880, loss: 0.2277 2023-03-03 16:54:19,104 - mmseg - INFO - Iter [22500/80000] lr: 3.750e-05, eta: 3:55:48, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.5197, loss: 0.2150 2023-03-03 16:54:29,059 - mmseg - INFO - Iter [22550/80000] lr: 3.750e-05, eta: 3:55:29, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2046, decode.acc_seg: 91.9171, loss: 0.2046 2023-03-03 16:54:39,287 - mmseg - INFO - Iter [22600/80000] lr: 3.750e-05, eta: 3:55:12, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2195, decode.acc_seg: 91.2643, loss: 0.2195 2023-03-03 16:54:49,259 - mmseg - INFO - Iter [22650/80000] lr: 3.750e-05, eta: 3:54:54, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2217, decode.acc_seg: 91.0758, loss: 0.2217 2023-03-03 16:54:59,367 - mmseg - INFO - Iter [22700/80000] lr: 3.750e-05, eta: 3:54:36, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2272, decode.acc_seg: 91.1456, loss: 0.2272 2023-03-03 16:55:11,986 - mmseg - INFO - Iter [22750/80000] lr: 3.750e-05, eta: 3:54:25, time: 0.252, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2257, decode.acc_seg: 91.0312, loss: 0.2257 2023-03-03 16:55:21,866 - mmseg - INFO - Iter [22800/80000] lr: 3.750e-05, eta: 3:54:06, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2210, decode.acc_seg: 91.3400, loss: 0.2210 2023-03-03 16:55:31,840 - mmseg - INFO - Iter [22850/80000] lr: 3.750e-05, eta: 3:53:48, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2271, decode.acc_seg: 91.2044, loss: 0.2271 2023-03-03 16:55:41,758 - mmseg - INFO - Iter [22900/80000] lr: 3.750e-05, eta: 3:53:30, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.5056, loss: 0.2135 2023-03-03 16:55:52,216 - mmseg - INFO - Iter [22950/80000] lr: 3.750e-05, eta: 3:53:13, time: 0.209, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2185, decode.acc_seg: 91.4030, loss: 0.2185 2023-03-03 16:56:02,212 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:56:02,212 - mmseg - INFO - Iter [23000/80000] lr: 3.750e-05, eta: 3:52:55, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2209, decode.acc_seg: 91.2812, loss: 0.2209 2023-03-03 16:56:12,157 - mmseg - INFO - Iter [23050/80000] lr: 3.750e-05, eta: 3:52:37, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2242, decode.acc_seg: 91.1019, loss: 0.2242 2023-03-03 16:56:22,033 - mmseg - INFO - Iter [23100/80000] lr: 3.750e-05, eta: 3:52:19, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2275, decode.acc_seg: 90.9519, loss: 0.2275 2023-03-03 16:56:32,068 - mmseg - INFO - Iter [23150/80000] lr: 3.750e-05, eta: 3:52:02, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2224, decode.acc_seg: 91.1203, loss: 0.2224 2023-03-03 16:56:41,946 - mmseg - INFO - Iter [23200/80000] lr: 3.750e-05, eta: 3:51:44, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2146, decode.acc_seg: 91.4138, loss: 0.2146 2023-03-03 16:56:51,821 - mmseg - INFO - Iter [23250/80000] lr: 3.750e-05, eta: 3:51:26, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2274, decode.acc_seg: 91.0670, loss: 0.2274 2023-03-03 16:57:01,921 - mmseg - INFO - Iter [23300/80000] lr: 3.750e-05, eta: 3:51:08, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.4664, loss: 0.2150 2023-03-03 16:57:14,600 - mmseg - INFO - Iter [23350/80000] lr: 3.750e-05, eta: 3:50:57, time: 0.254, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2189, decode.acc_seg: 91.2940, loss: 0.2189 2023-03-03 16:57:24,514 - mmseg - INFO - Iter [23400/80000] lr: 3.750e-05, eta: 3:50:39, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2152, decode.acc_seg: 91.4631, loss: 0.2152 2023-03-03 16:57:34,522 - mmseg - INFO - Iter [23450/80000] lr: 3.750e-05, eta: 3:50:22, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2227, decode.acc_seg: 91.0777, loss: 0.2227 2023-03-03 16:57:44,587 - mmseg - INFO - Iter [23500/80000] lr: 3.750e-05, eta: 3:50:04, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2188, decode.acc_seg: 91.3655, loss: 0.2188 2023-03-03 16:57:54,582 - mmseg - INFO - Iter [23550/80000] lr: 3.750e-05, eta: 3:49:47, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2245, decode.acc_seg: 91.0526, loss: 0.2245 2023-03-03 16:58:04,641 - mmseg - INFO - Iter [23600/80000] lr: 3.750e-05, eta: 3:49:29, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.1176, loss: 0.2211 2023-03-03 16:58:14,757 - mmseg - INFO - Iter [23650/80000] lr: 3.750e-05, eta: 3:49:12, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2274, decode.acc_seg: 90.8747, loss: 0.2274 2023-03-03 16:58:24,678 - mmseg - INFO - Iter [23700/80000] lr: 3.750e-05, eta: 3:48:54, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2182, decode.acc_seg: 91.3642, loss: 0.2182 2023-03-03 16:58:34,708 - mmseg - INFO - Iter [23750/80000] lr: 3.750e-05, eta: 3:48:37, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2196, decode.acc_seg: 91.2719, loss: 0.2196 2023-03-03 16:58:44,777 - mmseg - INFO - Iter [23800/80000] lr: 3.750e-05, eta: 3:48:20, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2134, decode.acc_seg: 91.5474, loss: 0.2134 2023-03-03 16:58:54,700 - mmseg - INFO - Iter [23850/80000] lr: 3.750e-05, eta: 3:48:02, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.5910, loss: 0.2140 2023-03-03 16:59:04,581 - mmseg - INFO - Iter [23900/80000] lr: 3.750e-05, eta: 3:47:45, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2158, decode.acc_seg: 91.4141, loss: 0.2158 2023-03-03 16:59:14,495 - mmseg - INFO - Iter [23950/80000] lr: 3.750e-05, eta: 3:47:27, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2194, decode.acc_seg: 91.2466, loss: 0.2194 2023-03-03 16:59:27,274 - mmseg - INFO - Saving checkpoint at 24000 iterations 2023-03-03 16:59:28,149 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:59:28,150 - mmseg - INFO - Iter [24000/80000] lr: 3.750e-05, eta: 3:47:19, time: 0.273, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2213, decode.acc_seg: 91.1293, loss: 0.2213 2023-03-03 16:59:42,997 - mmseg - INFO - per class results: 2023-03-03 16:59:43,003 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 75.53 | 88.35 | | building | 82.31 | 92.97 | | sky | 94.03 | 97.51 | | floor | 78.76 | 90.52 | | tree | 72.63 | 88.57 | | ceiling | 82.06 | 91.55 | | road | 80.53 | 86.55 | | bed | 87.44 | 95.59 | | windowpane | 59.4 | 76.31 | | grass | 65.42 | 81.76 | | cabinet | 58.9 | 71.16 | | sidewalk | 63.18 | 80.98 | | person | 77.72 | 90.08 | | earth | 32.85 | 50.16 | | door | 45.29 | 57.8 | | table | 59.46 | 75.48 | | mountain | 50.41 | 65.52 | | plant | 50.66 | 62.46 | | curtain | 71.07 | 83.49 | | chair | 54.61 | 71.24 | | car | 81.06 | 89.91 | | water | 44.86 | 60.36 | | painting | 72.43 | 84.09 | | sofa | 62.83 | 79.92 | | shelf | 37.56 | 53.15 | | house | 45.7 | 53.36 | | sea | 41.67 | 69.5 | | mirror | 63.1 | 71.76 | | rug | 54.78 | 60.65 | | field | 24.26 | 36.97 | | armchair | 41.55 | 61.51 | | seat | 58.16 | 76.48 | | fence | 31.93 | 41.37 | | desk | 46.73 | 68.63 | | rock | 29.78 | 45.94 | | wardrobe | 44.99 | 55.99 | | lamp | 61.57 | 76.15 | | bathtub | 71.44 | 77.72 | | railing | 28.74 | 43.18 | | cushion | 51.61 | 62.06 | | base | 20.1 | 30.51 | | box | 20.89 | 25.89 | | column | 43.94 | 54.48 | | signboard | 35.64 | 48.72 | | chest of drawers | 36.84 | 55.76 | | counter | 28.1 | 36.06 | | sand | 32.88 | 46.36 | | sink | 65.71 | 78.6 | | skyscraper | 61.75 | 70.13 | | fireplace | 70.07 | 87.14 | | refrigerator | 71.6 | 82.99 | | grandstand | 38.85 | 64.55 | | path | 13.78 | 18.53 | | stairs | 30.44 | 38.12 | | runway | 59.46 | 77.38 | | case | 45.89 | 68.6 | | pool table | 91.6 | 96.2 | | pillow | 52.71 | 63.04 | | screen door | 63.63 | 74.06 | | stairway | 30.6 | 40.11 | | river | 12.09 | 22.63 | | bridge | 60.89 | 65.61 | | bookcase | 36.82 | 43.39 | | blind | 41.35 | 48.1 | | coffee table | 58.7 | 76.81 | | toilet | 82.81 | 92.13 | | flower | 34.61 | 46.09 | | book | 46.18 | 62.9 | | hill | 4.91 | 6.72 | | bench | 36.79 | 47.94 | | countertop | 51.62 | 71.41 | | stove | 71.9 | 81.85 | | palm | 49.19 | 65.52 | | kitchen island | 43.95 | 67.85 | | computer | 54.69 | 66.4 | | swivel chair | 44.76 | 61.23 | | boat | 43.23 | 50.97 | | bar | 25.41 | 32.04 | | arcade machine | 31.52 | 35.97 | | hovel | 35.46 | 38.6 | | bus | 77.82 | 86.98 | | towel | 55.87 | 68.03 | | light | 53.83 | 61.45 | | truck | 31.03 | 41.42 | | tower | 36.44 | 45.49 | | chandelier | 67.59 | 81.88 | | awning | 24.18 | 27.12 | | streetlight | 25.82 | 32.18 | | booth | 40.54 | 46.74 | | television receiver | 66.94 | 79.06 | | airplane | 52.07 | 65.39 | | dirt track | 3.11 | 7.2 | | apparel | 28.66 | 40.5 | | pole | 23.85 | 35.69 | | land | 0.56 | 0.67 | | bannister | 10.05 | 13.18 | | escalator | 20.94 | 21.63 | | ottoman | 43.46 | 55.14 | | bottle | 12.12 | 18.66 | | buffet | 35.15 | 42.77 | | poster | 21.28 | 26.31 | | stage | 10.14 | 12.89 | | van | 39.67 | 56.13 | | ship | 61.81 | 70.62 | | fountain | 0.32 | 0.32 | | conveyer belt | 65.76 | 87.95 | | canopy | 14.77 | 17.17 | | washer | 62.9 | 64.86 | | plaything | 23.37 | 33.12 | | swimming pool | 27.8 | 33.34 | | stool | 38.74 | 53.68 | | barrel | 33.56 | 64.79 | | basket | 23.16 | 33.83 | | waterfall | 59.69 | 85.65 | | tent | 92.76 | 98.4 | | bag | 8.96 | 10.74 | | minibike | 51.47 | 60.32 | | cradle | 76.3 | 98.36 | | oven | 22.91 | 56.75 | | ball | 44.06 | 66.91 | | food | 51.51 | 61.0 | | step | 3.81 | 4.91 | | tank | 45.92 | 46.94 | | trade name | 19.08 | 20.79 | | microwave | 38.31 | 41.65 | | pot | 37.15 | 44.09 | | animal | 52.59 | 55.2 | | bicycle | 45.18 | 66.48 | | lake | 61.84 | 63.14 | | dishwasher | 71.89 | 75.92 | | screen | 58.21 | 69.67 | | blanket | 5.91 | 6.71 | | sculpture | 38.59 | 60.54 | | hood | 58.47 | 70.27 | | sconce | 40.8 | 47.56 | | vase | 32.17 | 46.57 | | traffic light | 22.24 | 27.73 | | tray | 5.17 | 8.25 | | ashcan | 40.27 | 52.88 | | fan | 56.66 | 70.03 | | pier | 21.79 | 26.32 | | crt screen | 2.88 | 4.93 | | plate | 37.99 | 47.85 | | monitor | 61.62 | 77.97 | | bulletin board | 33.67 | 46.85 | | shower | 2.73 | 6.99 | | radiator | 40.75 | 50.53 | | glass | 10.63 | 11.98 | | clock | 19.96 | 25.07 | | flag | 38.82 | 41.86 | +---------------------+-------+-------+ 2023-03-03 16:59:43,003 - mmseg - INFO - Summary: 2023-03-03 16:59:43,003 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.38 | 44.43 | 55.28 | +-------+-------+-------+ 2023-03-03 16:59:43,032 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_16000.pth was removed 2023-03-03 16:59:43,846 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_24000.pth. 2023-03-03 16:59:43,846 - mmseg - INFO - Best mIoU is 0.4443 at 24000 iter. 2023-03-03 16:59:43,846 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 16:59:43,847 - mmseg - INFO - Iter(val) [250] aAcc: 0.8138, mIoU: 0.4443, mAcc: 0.5528, IoU.background: nan, IoU.wall: 0.7553, IoU.building: 0.8231, IoU.sky: 0.9403, IoU.floor: 0.7876, IoU.tree: 0.7263, IoU.ceiling: 0.8206, IoU.road: 0.8053, IoU.bed : 0.8744, IoU.windowpane: 0.5940, IoU.grass: 0.6542, IoU.cabinet: 0.5890, IoU.sidewalk: 0.6318, IoU.person: 0.7772, IoU.earth: 0.3285, IoU.door: 0.4529, IoU.table: 0.5946, IoU.mountain: 0.5041, IoU.plant: 0.5066, IoU.curtain: 0.7107, IoU.chair: 0.5461, IoU.car: 0.8106, IoU.water: 0.4486, IoU.painting: 0.7243, IoU.sofa: 0.6283, IoU.shelf: 0.3756, IoU.house: 0.4570, IoU.sea: 0.4167, IoU.mirror: 0.6310, IoU.rug: 0.5478, IoU.field: 0.2426, IoU.armchair: 0.4155, IoU.seat: 0.5816, IoU.fence: 0.3193, IoU.desk: 0.4673, IoU.rock: 0.2978, IoU.wardrobe: 0.4499, IoU.lamp: 0.6157, IoU.bathtub: 0.7144, IoU.railing: 0.2874, IoU.cushion: 0.5161, IoU.base: 0.2010, IoU.box: 0.2089, IoU.column: 0.4394, IoU.signboard: 0.3564, IoU.chest of drawers: 0.3684, IoU.counter: 0.2810, IoU.sand: 0.3288, IoU.sink: 0.6571, IoU.skyscraper: 0.6175, IoU.fireplace: 0.7007, IoU.refrigerator: 0.7160, IoU.grandstand: 0.3885, IoU.path: 0.1378, IoU.stairs: 0.3044, IoU.runway: 0.5946, IoU.case: 0.4589, IoU.pool table: 0.9160, IoU.pillow: 0.5271, IoU.screen door: 0.6363, IoU.stairway: 0.3060, IoU.river: 0.1209, IoU.bridge: 0.6089, IoU.bookcase: 0.3682, IoU.blind: 0.4135, IoU.coffee table: 0.5870, IoU.toilet: 0.8281, IoU.flower: 0.3461, IoU.book: 0.4618, IoU.hill: 0.0491, IoU.bench: 0.3679, IoU.countertop: 0.5162, IoU.stove: 0.7190, IoU.palm: 0.4919, IoU.kitchen island: 0.4395, IoU.computer: 0.5469, IoU.swivel chair: 0.4476, IoU.boat: 0.4323, IoU.bar: 0.2541, IoU.arcade machine: 0.3152, IoU.hovel: 0.3546, IoU.bus: 0.7782, IoU.towel: 0.5587, IoU.light: 0.5383, IoU.truck: 0.3103, IoU.tower: 0.3644, IoU.chandelier: 0.6759, IoU.awning: 0.2418, IoU.streetlight: 0.2582, IoU.booth: 0.4054, IoU.television receiver: 0.6694, IoU.airplane: 0.5207, IoU.dirt track: 0.0311, IoU.apparel: 0.2866, IoU.pole: 0.2385, IoU.land: 0.0056, IoU.bannister: 0.1005, IoU.escalator: 0.2094, IoU.ottoman: 0.4346, IoU.bottle: 0.1212, IoU.buffet: 0.3515, IoU.poster: 0.2128, IoU.stage: 0.1014, IoU.van: 0.3967, IoU.ship: 0.6181, IoU.fountain: 0.0032, IoU.conveyer belt: 0.6576, IoU.canopy: 0.1477, IoU.washer: 0.6290, IoU.plaything: 0.2337, IoU.swimming pool: 0.2780, IoU.stool: 0.3874, IoU.barrel: 0.3356, IoU.basket: 0.2316, IoU.waterfall: 0.5969, IoU.tent: 0.9276, IoU.bag: 0.0896, IoU.minibike: 0.5147, IoU.cradle: 0.7630, IoU.oven: 0.2291, IoU.ball: 0.4406, IoU.food: 0.5151, IoU.step: 0.0381, IoU.tank: 0.4592, IoU.trade name: 0.1908, IoU.microwave: 0.3831, IoU.pot: 0.3715, IoU.animal: 0.5259, IoU.bicycle: 0.4518, IoU.lake: 0.6184, IoU.dishwasher: 0.7189, IoU.screen: 0.5821, IoU.blanket: 0.0591, IoU.sculpture: 0.3859, IoU.hood: 0.5847, IoU.sconce: 0.4080, IoU.vase: 0.3217, IoU.traffic light: 0.2224, IoU.tray: 0.0517, IoU.ashcan: 0.4027, IoU.fan: 0.5666, IoU.pier: 0.2179, IoU.crt screen: 0.0288, IoU.plate: 0.3799, IoU.monitor: 0.6162, IoU.bulletin board: 0.3367, IoU.shower: 0.0273, IoU.radiator: 0.4075, IoU.glass: 0.1063, IoU.clock: 0.1996, IoU.flag: 0.3882, Acc.background: nan, Acc.wall: 0.8835, Acc.building: 0.9297, Acc.sky: 0.9751, Acc.floor: 0.9052, Acc.tree: 0.8857, Acc.ceiling: 0.9155, Acc.road: 0.8655, Acc.bed : 0.9559, Acc.windowpane: 0.7631, Acc.grass: 0.8176, Acc.cabinet: 0.7116, Acc.sidewalk: 0.8098, Acc.person: 0.9008, Acc.earth: 0.5016, Acc.door: 0.5780, Acc.table: 0.7548, Acc.mountain: 0.6552, Acc.plant: 0.6246, Acc.curtain: 0.8349, Acc.chair: 0.7124, Acc.car: 0.8991, Acc.water: 0.6036, Acc.painting: 0.8409, Acc.sofa: 0.7992, Acc.shelf: 0.5315, Acc.house: 0.5336, Acc.sea: 0.6950, Acc.mirror: 0.7176, Acc.rug: 0.6065, Acc.field: 0.3697, Acc.armchair: 0.6151, Acc.seat: 0.7648, Acc.fence: 0.4137, Acc.desk: 0.6863, Acc.rock: 0.4594, Acc.wardrobe: 0.5599, Acc.lamp: 0.7615, Acc.bathtub: 0.7772, Acc.railing: 0.4318, Acc.cushion: 0.6206, Acc.base: 0.3051, Acc.box: 0.2589, Acc.column: 0.5448, Acc.signboard: 0.4872, Acc.chest of drawers: 0.5576, Acc.counter: 0.3606, Acc.sand: 0.4636, Acc.sink: 0.7860, Acc.skyscraper: 0.7013, Acc.fireplace: 0.8714, Acc.refrigerator: 0.8299, Acc.grandstand: 0.6455, Acc.path: 0.1853, Acc.stairs: 0.3812, Acc.runway: 0.7738, Acc.case: 0.6860, Acc.pool table: 0.9620, Acc.pillow: 0.6304, Acc.screen door: 0.7406, Acc.stairway: 0.4011, Acc.river: 0.2263, Acc.bridge: 0.6561, Acc.bookcase: 0.4339, Acc.blind: 0.4810, Acc.coffee table: 0.7681, Acc.toilet: 0.9213, Acc.flower: 0.4609, Acc.book: 0.6290, Acc.hill: 0.0672, Acc.bench: 0.4794, Acc.countertop: 0.7141, Acc.stove: 0.8185, Acc.palm: 0.6552, Acc.kitchen island: 0.6785, Acc.computer: 0.6640, Acc.swivel chair: 0.6123, Acc.boat: 0.5097, Acc.bar: 0.3204, Acc.arcade machine: 0.3597, Acc.hovel: 0.3860, Acc.bus: 0.8698, Acc.towel: 0.6803, Acc.light: 0.6145, Acc.truck: 0.4142, Acc.tower: 0.4549, Acc.chandelier: 0.8188, Acc.awning: 0.2712, Acc.streetlight: 0.3218, Acc.booth: 0.4674, Acc.television receiver: 0.7906, Acc.airplane: 0.6539, Acc.dirt track: 0.0720, Acc.apparel: 0.4050, Acc.pole: 0.3569, Acc.land: 0.0067, Acc.bannister: 0.1318, Acc.escalator: 0.2163, Acc.ottoman: 0.5514, Acc.bottle: 0.1866, Acc.buffet: 0.4277, Acc.poster: 0.2631, Acc.stage: 0.1289, Acc.van: 0.5613, Acc.ship: 0.7062, Acc.fountain: 0.0032, Acc.conveyer belt: 0.8795, Acc.canopy: 0.1717, Acc.washer: 0.6486, Acc.plaything: 0.3312, Acc.swimming pool: 0.3334, Acc.stool: 0.5368, Acc.barrel: 0.6479, Acc.basket: 0.3383, Acc.waterfall: 0.8565, Acc.tent: 0.9840, Acc.bag: 0.1074, Acc.minibike: 0.6032, Acc.cradle: 0.9836, Acc.oven: 0.5675, Acc.ball: 0.6691, Acc.food: 0.6100, Acc.step: 0.0491, Acc.tank: 0.4694, Acc.trade name: 0.2079, Acc.microwave: 0.4165, Acc.pot: 0.4409, Acc.animal: 0.5520, Acc.bicycle: 0.6648, Acc.lake: 0.6314, Acc.dishwasher: 0.7592, Acc.screen: 0.6967, Acc.blanket: 0.0671, Acc.sculpture: 0.6054, Acc.hood: 0.7027, Acc.sconce: 0.4756, Acc.vase: 0.4657, Acc.traffic light: 0.2773, Acc.tray: 0.0825, Acc.ashcan: 0.5288, Acc.fan: 0.7003, Acc.pier: 0.2632, Acc.crt screen: 0.0493, Acc.plate: 0.4785, Acc.monitor: 0.7797, Acc.bulletin board: 0.4685, Acc.shower: 0.0699, Acc.radiator: 0.5053, Acc.glass: 0.1198, Acc.clock: 0.2507, Acc.flag: 0.4186 2023-03-03 16:59:54,050 - mmseg - INFO - Iter [24050/80000] lr: 3.750e-05, eta: 3:47:38, time: 0.518, data_time: 0.321, memory: 67202, decode.loss_ce: 0.2139, decode.acc_seg: 91.4738, loss: 0.2139 2023-03-03 17:00:04,300 - mmseg - INFO - Iter [24100/80000] lr: 3.750e-05, eta: 3:47:22, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2283, decode.acc_seg: 91.0413, loss: 0.2283 2023-03-03 17:00:14,381 - mmseg - INFO - Iter [24150/80000] lr: 3.750e-05, eta: 3:47:04, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2219, decode.acc_seg: 91.1314, loss: 0.2219 2023-03-03 17:00:24,342 - mmseg - INFO - Iter [24200/80000] lr: 3.750e-05, eta: 3:46:47, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2138, decode.acc_seg: 91.4437, loss: 0.2138 2023-03-03 17:00:34,385 - mmseg - INFO - Iter [24250/80000] lr: 3.750e-05, eta: 3:46:30, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.5347, loss: 0.2153 2023-03-03 17:00:44,533 - mmseg - INFO - Iter [24300/80000] lr: 3.750e-05, eta: 3:46:13, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2177, decode.acc_seg: 91.1528, loss: 0.2177 2023-03-03 17:00:54,482 - mmseg - INFO - Iter [24350/80000] lr: 3.750e-05, eta: 3:45:56, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2260, decode.acc_seg: 91.0919, loss: 0.2260 2023-03-03 17:01:04,464 - mmseg - INFO - Iter [24400/80000] lr: 3.750e-05, eta: 3:45:39, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2130, decode.acc_seg: 91.5197, loss: 0.2130 2023-03-03 17:01:14,638 - mmseg - INFO - Iter [24450/80000] lr: 3.750e-05, eta: 3:45:22, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.4566, loss: 0.2145 2023-03-03 17:01:24,688 - mmseg - INFO - Iter [24500/80000] lr: 3.750e-05, eta: 3:45:05, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2207, decode.acc_seg: 91.1380, loss: 0.2207 2023-03-03 17:01:34,762 - mmseg - INFO - Iter [24550/80000] lr: 3.750e-05, eta: 3:44:48, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2319, decode.acc_seg: 90.9511, loss: 0.2319 2023-03-03 17:01:44,737 - mmseg - INFO - Iter [24600/80000] lr: 3.750e-05, eta: 3:44:31, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.2622, loss: 0.2211 2023-03-03 17:01:57,194 - mmseg - INFO - Iter [24650/80000] lr: 3.750e-05, eta: 3:44:19, time: 0.249, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2134, decode.acc_seg: 91.5098, loss: 0.2134 2023-03-03 17:02:07,364 - mmseg - INFO - Iter [24700/80000] lr: 3.750e-05, eta: 3:44:03, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2183, decode.acc_seg: 91.3844, loss: 0.2183 2023-03-03 17:02:17,378 - mmseg - INFO - Iter [24750/80000] lr: 3.750e-05, eta: 3:43:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2216, decode.acc_seg: 91.1603, loss: 0.2216 2023-03-03 17:02:27,378 - mmseg - INFO - Iter [24800/80000] lr: 3.750e-05, eta: 3:43:29, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2232, decode.acc_seg: 91.0899, loss: 0.2232 2023-03-03 17:02:37,427 - mmseg - INFO - Iter [24850/80000] lr: 3.750e-05, eta: 3:43:12, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2216, decode.acc_seg: 91.3811, loss: 0.2216 2023-03-03 17:02:47,444 - mmseg - INFO - Iter [24900/80000] lr: 3.750e-05, eta: 3:42:55, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2188, decode.acc_seg: 91.2143, loss: 0.2188 2023-03-03 17:02:57,370 - mmseg - INFO - Iter [24950/80000] lr: 3.750e-05, eta: 3:42:38, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2154, decode.acc_seg: 91.4108, loss: 0.2154 2023-03-03 17:03:07,387 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:03:07,387 - mmseg - INFO - Iter [25000/80000] lr: 3.750e-05, eta: 3:42:21, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2094, decode.acc_seg: 91.5624, loss: 0.2094 2023-03-03 17:03:17,342 - mmseg - INFO - Iter [25050/80000] lr: 3.750e-05, eta: 3:42:05, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2222, decode.acc_seg: 91.1053, loss: 0.2222 2023-03-03 17:03:27,298 - mmseg - INFO - Iter [25100/80000] lr: 3.750e-05, eta: 3:41:48, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2240, decode.acc_seg: 91.0907, loss: 0.2240 2023-03-03 17:03:37,301 - mmseg - INFO - Iter [25150/80000] lr: 3.750e-05, eta: 3:41:31, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2180, decode.acc_seg: 91.2619, loss: 0.2180 2023-03-03 17:03:47,235 - mmseg - INFO - Iter [25200/80000] lr: 3.750e-05, eta: 3:41:14, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2241, decode.acc_seg: 91.0381, loss: 0.2241 2023-03-03 17:03:59,583 - mmseg - INFO - Iter [25250/80000] lr: 3.750e-05, eta: 3:41:02, time: 0.247, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2184, decode.acc_seg: 91.2880, loss: 0.2184 2023-03-03 17:04:09,528 - mmseg - INFO - Iter [25300/80000] lr: 3.750e-05, eta: 3:40:46, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2252, decode.acc_seg: 91.1342, loss: 0.2252 2023-03-03 17:04:19,474 - mmseg - INFO - Iter [25350/80000] lr: 3.750e-05, eta: 3:40:29, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2199, decode.acc_seg: 91.2874, loss: 0.2199 2023-03-03 17:04:29,672 - mmseg - INFO - Iter [25400/80000] lr: 3.750e-05, eta: 3:40:13, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.5046, loss: 0.2127 2023-03-03 17:04:39,900 - mmseg - INFO - Iter [25450/80000] lr: 3.750e-05, eta: 3:39:57, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.3311, loss: 0.2181 2023-03-03 17:04:50,172 - mmseg - INFO - Iter [25500/80000] lr: 3.750e-05, eta: 3:39:41, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2199, decode.acc_seg: 91.2722, loss: 0.2199 2023-03-03 17:05:00,267 - mmseg - INFO - Iter [25550/80000] lr: 3.750e-05, eta: 3:39:24, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2197, decode.acc_seg: 91.3462, loss: 0.2197 2023-03-03 17:05:10,141 - mmseg - INFO - Iter [25600/80000] lr: 3.750e-05, eta: 3:39:07, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2179, decode.acc_seg: 91.4099, loss: 0.2179 2023-03-03 17:05:20,067 - mmseg - INFO - Iter [25650/80000] lr: 3.750e-05, eta: 3:38:51, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2178, decode.acc_seg: 91.3655, loss: 0.2178 2023-03-03 17:05:29,982 - mmseg - INFO - Iter [25700/80000] lr: 3.750e-05, eta: 3:38:34, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2187, decode.acc_seg: 91.3343, loss: 0.2187 2023-03-03 17:05:39,897 - mmseg - INFO - Iter [25750/80000] lr: 3.750e-05, eta: 3:38:18, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.3964, loss: 0.2181 2023-03-03 17:05:49,791 - mmseg - INFO - Iter [25800/80000] lr: 3.750e-05, eta: 3:38:01, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2241, decode.acc_seg: 91.1879, loss: 0.2241 2023-03-03 17:05:59,683 - mmseg - INFO - Iter [25850/80000] lr: 3.750e-05, eta: 3:37:44, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2309, decode.acc_seg: 90.9807, loss: 0.2309 2023-03-03 17:06:12,423 - mmseg - INFO - Iter [25900/80000] lr: 3.750e-05, eta: 3:37:34, time: 0.255, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2168, decode.acc_seg: 91.2420, loss: 0.2168 2023-03-03 17:06:22,480 - mmseg - INFO - Iter [25950/80000] lr: 3.750e-05, eta: 3:37:17, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2169, decode.acc_seg: 91.5366, loss: 0.2169 2023-03-03 17:06:32,479 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:06:32,480 - mmseg - INFO - Iter [26000/80000] lr: 3.750e-05, eta: 3:37:01, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2195, decode.acc_seg: 91.3057, loss: 0.2195 2023-03-03 17:06:42,331 - mmseg - INFO - Iter [26050/80000] lr: 3.750e-05, eta: 3:36:44, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2097, decode.acc_seg: 91.5038, loss: 0.2097 2023-03-03 17:06:52,239 - mmseg - INFO - Iter [26100/80000] lr: 3.750e-05, eta: 3:36:28, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2168, decode.acc_seg: 91.5772, loss: 0.2168 2023-03-03 17:07:02,205 - mmseg - INFO - Iter [26150/80000] lr: 3.750e-05, eta: 3:36:12, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2180, decode.acc_seg: 91.3143, loss: 0.2180 2023-03-03 17:07:12,139 - mmseg - INFO - Iter [26200/80000] lr: 3.750e-05, eta: 3:35:55, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2293, decode.acc_seg: 90.9196, loss: 0.2293 2023-03-03 17:07:22,229 - mmseg - INFO - Iter [26250/80000] lr: 3.750e-05, eta: 3:35:39, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2136, decode.acc_seg: 91.3905, loss: 0.2136 2023-03-03 17:07:32,107 - mmseg - INFO - Iter [26300/80000] lr: 3.750e-05, eta: 3:35:23, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.2058, loss: 0.2211 2023-03-03 17:07:42,222 - mmseg - INFO - Iter [26350/80000] lr: 3.750e-05, eta: 3:35:07, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2277, decode.acc_seg: 91.0490, loss: 0.2277 2023-03-03 17:07:52,098 - mmseg - INFO - Iter [26400/80000] lr: 3.750e-05, eta: 3:34:50, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2201, decode.acc_seg: 91.2153, loss: 0.2201 2023-03-03 17:08:02,111 - mmseg - INFO - Iter [26450/80000] lr: 3.750e-05, eta: 3:34:34, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2206, decode.acc_seg: 91.3892, loss: 0.2206 2023-03-03 17:08:11,951 - mmseg - INFO - Iter [26500/80000] lr: 3.750e-05, eta: 3:34:18, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2169, decode.acc_seg: 91.3108, loss: 0.2169 2023-03-03 17:08:24,579 - mmseg - INFO - Iter [26550/80000] lr: 3.750e-05, eta: 3:34:07, time: 0.253, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2172, decode.acc_seg: 91.4349, loss: 0.2172 2023-03-03 17:08:34,525 - mmseg - INFO - Iter [26600/80000] lr: 3.750e-05, eta: 3:33:51, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.6462, loss: 0.2125 2023-03-03 17:08:44,489 - mmseg - INFO - Iter [26650/80000] lr: 3.750e-05, eta: 3:33:35, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2281, decode.acc_seg: 90.9118, loss: 0.2281 2023-03-03 17:08:54,391 - mmseg - INFO - Iter [26700/80000] lr: 3.750e-05, eta: 3:33:19, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2134, decode.acc_seg: 91.5580, loss: 0.2134 2023-03-03 17:09:04,388 - mmseg - INFO - Iter [26750/80000] lr: 3.750e-05, eta: 3:33:03, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2274, decode.acc_seg: 90.8985, loss: 0.2274 2023-03-03 17:09:14,352 - mmseg - INFO - Iter [26800/80000] lr: 3.750e-05, eta: 3:32:46, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2274, decode.acc_seg: 90.9864, loss: 0.2274 2023-03-03 17:09:24,376 - mmseg - INFO - Iter [26850/80000] lr: 3.750e-05, eta: 3:32:31, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2180, decode.acc_seg: 91.3403, loss: 0.2180 2023-03-03 17:09:34,257 - mmseg - INFO - Iter [26900/80000] lr: 3.750e-05, eta: 3:32:14, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.5420, loss: 0.2181 2023-03-03 17:09:44,305 - mmseg - INFO - Iter [26950/80000] lr: 3.750e-05, eta: 3:31:59, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2174, decode.acc_seg: 91.2546, loss: 0.2174 2023-03-03 17:09:54,382 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:09:54,382 - mmseg - INFO - Iter [27000/80000] lr: 3.750e-05, eta: 3:31:43, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.1760, loss: 0.2212 2023-03-03 17:10:04,473 - mmseg - INFO - Iter [27050/80000] lr: 3.750e-05, eta: 3:31:27, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2256, decode.acc_seg: 91.1215, loss: 0.2256 2023-03-03 17:10:14,496 - mmseg - INFO - Iter [27100/80000] lr: 3.750e-05, eta: 3:31:11, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2201, decode.acc_seg: 91.1528, loss: 0.2201 2023-03-03 17:10:27,030 - mmseg - INFO - Iter [27150/80000] lr: 3.750e-05, eta: 3:31:00, time: 0.251, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2183, decode.acc_seg: 91.3536, loss: 0.2183 2023-03-03 17:10:37,272 - mmseg - INFO - Iter [27200/80000] lr: 3.750e-05, eta: 3:30:45, time: 0.205, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.4672, loss: 0.2144 2023-03-03 17:10:47,231 - mmseg - INFO - Iter [27250/80000] lr: 3.750e-05, eta: 3:30:29, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2176, decode.acc_seg: 91.3776, loss: 0.2176 2023-03-03 17:10:57,244 - mmseg - INFO - Iter [27300/80000] lr: 3.750e-05, eta: 3:30:13, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2166, decode.acc_seg: 91.5050, loss: 0.2166 2023-03-03 17:11:07,323 - mmseg - INFO - Iter [27350/80000] lr: 3.750e-05, eta: 3:29:58, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2182, decode.acc_seg: 91.5274, loss: 0.2182 2023-03-03 17:11:17,515 - mmseg - INFO - Iter [27400/80000] lr: 3.750e-05, eta: 3:29:43, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.3601, loss: 0.2143 2023-03-03 17:11:27,565 - mmseg - INFO - Iter [27450/80000] lr: 3.750e-05, eta: 3:29:27, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2279, decode.acc_seg: 90.9250, loss: 0.2279 2023-03-03 17:11:37,707 - mmseg - INFO - Iter [27500/80000] lr: 3.750e-05, eta: 3:29:11, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2186, decode.acc_seg: 91.2141, loss: 0.2186 2023-03-03 17:11:47,869 - mmseg - INFO - Iter [27550/80000] lr: 3.750e-05, eta: 3:28:56, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2285, decode.acc_seg: 91.1172, loss: 0.2285 2023-03-03 17:11:57,961 - mmseg - INFO - Iter [27600/80000] lr: 3.750e-05, eta: 3:28:41, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.3022, loss: 0.2141 2023-03-03 17:12:08,006 - mmseg - INFO - Iter [27650/80000] lr: 3.750e-05, eta: 3:28:25, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2232, decode.acc_seg: 91.1679, loss: 0.2232 2023-03-03 17:12:18,004 - mmseg - INFO - Iter [27700/80000] lr: 3.750e-05, eta: 3:28:09, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.4401, loss: 0.2153 2023-03-03 17:12:28,112 - mmseg - INFO - Iter [27750/80000] lr: 3.750e-05, eta: 3:27:54, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2188, decode.acc_seg: 91.2807, loss: 0.2188 2023-03-03 17:12:40,525 - mmseg - INFO - Iter [27800/80000] lr: 3.750e-05, eta: 3:27:43, time: 0.248, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.3025, loss: 0.2144 2023-03-03 17:12:50,625 - mmseg - INFO - Iter [27850/80000] lr: 3.750e-05, eta: 3:27:28, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2216, decode.acc_seg: 91.0637, loss: 0.2216 2023-03-03 17:13:00,767 - mmseg - INFO - Iter [27900/80000] lr: 3.750e-05, eta: 3:27:12, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2250, decode.acc_seg: 91.0940, loss: 0.2250 2023-03-03 17:13:10,680 - mmseg - INFO - Iter [27950/80000] lr: 3.750e-05, eta: 3:26:57, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2165, decode.acc_seg: 91.3626, loss: 0.2165 2023-03-03 17:13:20,684 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:13:20,684 - mmseg - INFO - Iter [28000/80000] lr: 3.750e-05, eta: 3:26:41, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2208, decode.acc_seg: 91.1704, loss: 0.2208 2023-03-03 17:13:30,688 - mmseg - INFO - Iter [28050/80000] lr: 3.750e-05, eta: 3:26:26, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2243, decode.acc_seg: 91.1677, loss: 0.2243 2023-03-03 17:13:40,767 - mmseg - INFO - Iter [28100/80000] lr: 3.750e-05, eta: 3:26:10, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2208, decode.acc_seg: 91.2761, loss: 0.2208 2023-03-03 17:13:50,626 - mmseg - INFO - Iter [28150/80000] lr: 3.750e-05, eta: 3:25:55, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.3672, loss: 0.2144 2023-03-03 17:14:00,486 - mmseg - INFO - Iter [28200/80000] lr: 3.750e-05, eta: 3:25:39, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2158, decode.acc_seg: 91.3854, loss: 0.2158 2023-03-03 17:14:10,370 - mmseg - INFO - Iter [28250/80000] lr: 3.750e-05, eta: 3:25:23, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2189, decode.acc_seg: 91.1645, loss: 0.2189 2023-03-03 17:14:20,401 - mmseg - INFO - Iter [28300/80000] lr: 3.750e-05, eta: 3:25:08, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.3989, loss: 0.2142 2023-03-03 17:14:30,311 - mmseg - INFO - Iter [28350/80000] lr: 3.750e-05, eta: 3:24:52, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.2640, loss: 0.2212 2023-03-03 17:14:43,057 - mmseg - INFO - Iter [28400/80000] lr: 3.750e-05, eta: 3:24:42, time: 0.255, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.2850, loss: 0.2181 2023-03-03 17:14:53,113 - mmseg - INFO - Iter [28450/80000] lr: 3.750e-05, eta: 3:24:27, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.4835, loss: 0.2151 2023-03-03 17:15:03,089 - mmseg - INFO - Iter [28500/80000] lr: 3.750e-05, eta: 3:24:11, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2293, decode.acc_seg: 90.8546, loss: 0.2293 2023-03-03 17:15:13,066 - mmseg - INFO - Iter [28550/80000] lr: 3.750e-05, eta: 3:23:56, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2204, decode.acc_seg: 91.1173, loss: 0.2204 2023-03-03 17:15:23,240 - mmseg - INFO - Iter [28600/80000] lr: 3.750e-05, eta: 3:23:41, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.6208, loss: 0.2125 2023-03-03 17:15:33,295 - mmseg - INFO - Iter [28650/80000] lr: 3.750e-05, eta: 3:23:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.5325, loss: 0.2124 2023-03-03 17:15:43,305 - mmseg - INFO - Iter [28700/80000] lr: 3.750e-05, eta: 3:23:11, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.0724, loss: 0.2211 2023-03-03 17:15:53,364 - mmseg - INFO - Iter [28750/80000] lr: 3.750e-05, eta: 3:22:56, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2180, decode.acc_seg: 91.3942, loss: 0.2180 2023-03-03 17:16:03,340 - mmseg - INFO - Iter [28800/80000] lr: 3.750e-05, eta: 3:22:40, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2246, decode.acc_seg: 91.0472, loss: 0.2246 2023-03-03 17:16:13,272 - mmseg - INFO - Iter [28850/80000] lr: 3.750e-05, eta: 3:22:25, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.3549, loss: 0.2145 2023-03-03 17:16:23,284 - mmseg - INFO - Iter [28900/80000] lr: 3.750e-05, eta: 3:22:10, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.6002, loss: 0.2105 2023-03-03 17:16:33,298 - mmseg - INFO - Iter [28950/80000] lr: 3.750e-05, eta: 3:21:55, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2219, decode.acc_seg: 91.2503, loss: 0.2219 2023-03-03 17:16:43,183 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:16:43,183 - mmseg - INFO - Iter [29000/80000] lr: 3.750e-05, eta: 3:21:39, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2227, decode.acc_seg: 91.1815, loss: 0.2227 2023-03-03 17:16:55,694 - mmseg - INFO - Iter [29050/80000] lr: 3.750e-05, eta: 3:21:29, time: 0.250, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2197, decode.acc_seg: 91.3284, loss: 0.2197 2023-03-03 17:17:05,572 - mmseg - INFO - Iter [29100/80000] lr: 3.750e-05, eta: 3:21:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.5100, loss: 0.2124 2023-03-03 17:17:15,495 - mmseg - INFO - Iter [29150/80000] lr: 3.750e-05, eta: 3:20:58, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.5064, loss: 0.2141 2023-03-03 17:17:25,531 - mmseg - INFO - Iter [29200/80000] lr: 3.750e-05, eta: 3:20:43, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2271, decode.acc_seg: 91.0199, loss: 0.2271 2023-03-03 17:17:35,571 - mmseg - INFO - Iter [29250/80000] lr: 3.750e-05, eta: 3:20:28, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2163, decode.acc_seg: 91.3480, loss: 0.2163 2023-03-03 17:17:45,782 - mmseg - INFO - Iter [29300/80000] lr: 3.750e-05, eta: 3:20:13, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2209, decode.acc_seg: 91.1075, loss: 0.2209 2023-03-03 17:17:55,967 - mmseg - INFO - Iter [29350/80000] lr: 3.750e-05, eta: 3:19:59, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2131, decode.acc_seg: 91.4409, loss: 0.2131 2023-03-03 17:18:05,920 - mmseg - INFO - Iter [29400/80000] lr: 3.750e-05, eta: 3:19:44, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2106, decode.acc_seg: 91.7634, loss: 0.2106 2023-03-03 17:18:15,893 - mmseg - INFO - Iter [29450/80000] lr: 3.750e-05, eta: 3:19:28, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.2548, loss: 0.2181 2023-03-03 17:18:25,924 - mmseg - INFO - Iter [29500/80000] lr: 3.750e-05, eta: 3:19:14, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2186, decode.acc_seg: 91.3612, loss: 0.2186 2023-03-03 17:18:35,847 - mmseg - INFO - Iter [29550/80000] lr: 3.750e-05, eta: 3:18:58, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2207, decode.acc_seg: 91.2259, loss: 0.2207 2023-03-03 17:18:45,807 - mmseg - INFO - Iter [29600/80000] lr: 3.750e-05, eta: 3:18:43, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2294, decode.acc_seg: 90.9400, loss: 0.2294 2023-03-03 17:18:55,878 - mmseg - INFO - Iter [29650/80000] lr: 3.750e-05, eta: 3:18:29, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2134, decode.acc_seg: 91.4488, loss: 0.2134 2023-03-03 17:19:08,363 - mmseg - INFO - Iter [29700/80000] lr: 3.750e-05, eta: 3:18:18, time: 0.250, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2254, decode.acc_seg: 91.0316, loss: 0.2254 2023-03-03 17:19:18,348 - mmseg - INFO - Iter [29750/80000] lr: 3.750e-05, eta: 3:18:03, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2174, decode.acc_seg: 91.2487, loss: 0.2174 2023-03-03 17:19:28,513 - mmseg - INFO - Iter [29800/80000] lr: 3.750e-05, eta: 3:17:48, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2216, decode.acc_seg: 91.2197, loss: 0.2216 2023-03-03 17:19:38,973 - mmseg - INFO - Iter [29850/80000] lr: 3.750e-05, eta: 3:17:34, time: 0.209, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2206, decode.acc_seg: 91.1607, loss: 0.2206 2023-03-03 17:19:48,846 - mmseg - INFO - Iter [29900/80000] lr: 3.750e-05, eta: 3:17:19, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.3752, loss: 0.2122 2023-03-03 17:19:58,799 - mmseg - INFO - Iter [29950/80000] lr: 3.750e-05, eta: 3:17:04, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2162, decode.acc_seg: 91.3060, loss: 0.2162 2023-03-03 17:20:08,916 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:20:08,916 - mmseg - INFO - Iter [30000/80000] lr: 3.750e-05, eta: 3:16:50, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2232, decode.acc_seg: 90.9896, loss: 0.2232 2023-03-03 17:20:18,993 - mmseg - INFO - Iter [30050/80000] lr: 1.875e-05, eta: 3:16:35, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2129, decode.acc_seg: 91.4878, loss: 0.2129 2023-03-03 17:20:29,003 - mmseg - INFO - Iter [30100/80000] lr: 1.875e-05, eta: 3:16:20, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2174, decode.acc_seg: 91.3152, loss: 0.2174 2023-03-03 17:20:39,024 - mmseg - INFO - Iter [30150/80000] lr: 1.875e-05, eta: 3:16:05, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2155, decode.acc_seg: 91.5115, loss: 0.2155 2023-03-03 17:20:49,096 - mmseg - INFO - Iter [30200/80000] lr: 1.875e-05, eta: 3:15:51, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2179, decode.acc_seg: 91.4060, loss: 0.2179 2023-03-03 17:20:59,197 - mmseg - INFO - Iter [30250/80000] lr: 1.875e-05, eta: 3:15:36, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.6365, loss: 0.2105 2023-03-03 17:21:11,943 - mmseg - INFO - Iter [30300/80000] lr: 1.875e-05, eta: 3:15:26, time: 0.255, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.4103, loss: 0.2143 2023-03-03 17:21:21,934 - mmseg - INFO - Iter [30350/80000] lr: 1.875e-05, eta: 3:15:11, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2137, decode.acc_seg: 91.4863, loss: 0.2137 2023-03-03 17:21:31,843 - mmseg - INFO - Iter [30400/80000] lr: 1.875e-05, eta: 3:14:56, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2244, decode.acc_seg: 91.1743, loss: 0.2244 2023-03-03 17:21:41,838 - mmseg - INFO - Iter [30450/80000] lr: 1.875e-05, eta: 3:14:42, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2215, decode.acc_seg: 91.1969, loss: 0.2215 2023-03-03 17:21:51,917 - mmseg - INFO - Iter [30500/80000] lr: 1.875e-05, eta: 3:14:27, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2208, decode.acc_seg: 91.1237, loss: 0.2208 2023-03-03 17:22:01,842 - mmseg - INFO - Iter [30550/80000] lr: 1.875e-05, eta: 3:14:12, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.4445, loss: 0.2141 2023-03-03 17:22:11,774 - mmseg - INFO - Iter [30600/80000] lr: 1.875e-05, eta: 3:13:57, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.5887, loss: 0.2128 2023-03-03 17:22:21,881 - mmseg - INFO - Iter [30650/80000] lr: 1.875e-05, eta: 3:13:43, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2097, decode.acc_seg: 91.6034, loss: 0.2097 2023-03-03 17:22:31,786 - mmseg - INFO - Iter [30700/80000] lr: 1.875e-05, eta: 3:13:28, time: 0.198, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2056, decode.acc_seg: 91.7677, loss: 0.2056 2023-03-03 17:22:41,749 - mmseg - INFO - Iter [30750/80000] lr: 1.875e-05, eta: 3:13:13, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.5025, loss: 0.2153 2023-03-03 17:22:51,757 - mmseg - INFO - Iter [30800/80000] lr: 1.875e-05, eta: 3:12:59, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.4770, loss: 0.2117 2023-03-03 17:23:01,761 - mmseg - INFO - Iter [30850/80000] lr: 1.875e-05, eta: 3:12:44, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2268, decode.acc_seg: 90.9666, loss: 0.2268 2023-03-03 17:23:11,903 - mmseg - INFO - Iter [30900/80000] lr: 1.875e-05, eta: 3:12:30, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.6066, loss: 0.2122 2023-03-03 17:23:24,411 - mmseg - INFO - Iter [30950/80000] lr: 1.875e-05, eta: 3:12:19, time: 0.250, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2163, decode.acc_seg: 91.5013, loss: 0.2163 2023-03-03 17:23:34,266 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:23:34,267 - mmseg - INFO - Iter [31000/80000] lr: 1.875e-05, eta: 3:12:05, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.5320, loss: 0.2143 2023-03-03 17:23:44,187 - mmseg - INFO - Iter [31050/80000] lr: 1.875e-05, eta: 3:11:50, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2156, decode.acc_seg: 91.2845, loss: 0.2156 2023-03-03 17:23:54,157 - mmseg - INFO - Iter [31100/80000] lr: 1.875e-05, eta: 3:11:35, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2075, decode.acc_seg: 91.7751, loss: 0.2075 2023-03-03 17:24:04,017 - mmseg - INFO - Iter [31150/80000] lr: 1.875e-05, eta: 3:11:21, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.3174, loss: 0.2211 2023-03-03 17:24:13,958 - mmseg - INFO - Iter [31200/80000] lr: 1.875e-05, eta: 3:11:06, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2208, decode.acc_seg: 91.2079, loss: 0.2208 2023-03-03 17:24:24,052 - mmseg - INFO - Iter [31250/80000] lr: 1.875e-05, eta: 3:10:52, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2255, decode.acc_seg: 91.1589, loss: 0.2255 2023-03-03 17:24:33,947 - mmseg - INFO - Iter [31300/80000] lr: 1.875e-05, eta: 3:10:37, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.4990, loss: 0.2099 2023-03-03 17:24:44,167 - mmseg - INFO - Iter [31350/80000] lr: 1.875e-05, eta: 3:10:23, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2183, decode.acc_seg: 91.2578, loss: 0.2183 2023-03-03 17:24:54,113 - mmseg - INFO - Iter [31400/80000] lr: 1.875e-05, eta: 3:10:08, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2197, decode.acc_seg: 91.0637, loss: 0.2197 2023-03-03 17:25:04,272 - mmseg - INFO - Iter [31450/80000] lr: 1.875e-05, eta: 3:09:54, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2170, decode.acc_seg: 91.2530, loss: 0.2170 2023-03-03 17:25:14,208 - mmseg - INFO - Iter [31500/80000] lr: 1.875e-05, eta: 3:09:40, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.4820, loss: 0.2141 2023-03-03 17:25:24,227 - mmseg - INFO - Iter [31550/80000] lr: 1.875e-05, eta: 3:09:25, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.4177, loss: 0.2145 2023-03-03 17:25:36,746 - mmseg - INFO - Iter [31600/80000] lr: 1.875e-05, eta: 3:09:15, time: 0.250, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.2383, loss: 0.2211 2023-03-03 17:25:46,848 - mmseg - INFO - Iter [31650/80000] lr: 1.875e-05, eta: 3:09:01, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2190, decode.acc_seg: 91.4290, loss: 0.2190 2023-03-03 17:25:56,831 - mmseg - INFO - Iter [31700/80000] lr: 1.875e-05, eta: 3:08:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2207, decode.acc_seg: 91.2592, loss: 0.2207 2023-03-03 17:26:07,117 - mmseg - INFO - Iter [31750/80000] lr: 1.875e-05, eta: 3:08:32, time: 0.206, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2173, decode.acc_seg: 91.4247, loss: 0.2173 2023-03-03 17:26:17,131 - mmseg - INFO - Iter [31800/80000] lr: 1.875e-05, eta: 3:08:18, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.4886, loss: 0.2142 2023-03-03 17:26:27,157 - mmseg - INFO - Iter [31850/80000] lr: 1.875e-05, eta: 3:08:04, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2199, decode.acc_seg: 91.2362, loss: 0.2199 2023-03-03 17:26:37,357 - mmseg - INFO - Iter [31900/80000] lr: 1.875e-05, eta: 3:07:50, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2152, decode.acc_seg: 91.5376, loss: 0.2152 2023-03-03 17:26:47,342 - mmseg - INFO - Iter [31950/80000] lr: 1.875e-05, eta: 3:07:35, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.5918, loss: 0.2092 2023-03-03 17:26:57,231 - mmseg - INFO - Saving checkpoint at 32000 iterations 2023-03-03 17:26:58,219 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:26:58,219 - mmseg - INFO - Iter [32000/80000] lr: 1.875e-05, eta: 3:07:22, time: 0.218, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2197, decode.acc_seg: 91.2836, loss: 0.2197 2023-03-03 17:27:12,970 - mmseg - INFO - per class results: 2023-03-03 17:27:12,976 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 75.6 | 88.31 | | building | 82.39 | 93.14 | | sky | 94.07 | 97.31 | | floor | 79.01 | 89.71 | | tree | 73.1 | 88.13 | | ceiling | 82.17 | 91.71 | | road | 80.72 | 89.31 | | bed | 87.69 | 95.2 | | windowpane | 59.61 | 75.88 | | grass | 65.4 | 83.35 | | cabinet | 58.15 | 72.48 | | sidewalk | 64.74 | 79.75 | | person | 77.79 | 90.83 | | earth | 31.01 | 42.78 | | door | 45.44 | 59.66 | | table | 59.63 | 73.61 | | mountain | 51.46 | 67.75 | | plant | 50.45 | 62.96 | | curtain | 71.47 | 82.75 | | chair | 54.9 | 71.02 | | car | 80.86 | 90.14 | | water | 45.63 | 62.2 | | painting | 72.17 | 84.22 | | sofa | 61.16 | 83.09 | | shelf | 38.36 | 54.91 | | house | 46.46 | 56.33 | | sea | 42.63 | 68.71 | | mirror | 62.96 | 71.83 | | rug | 55.44 | 64.05 | | field | 22.8 | 35.59 | | armchair | 40.92 | 54.04 | | seat | 58.18 | 76.06 | | fence | 33.66 | 46.8 | | desk | 49.72 | 67.07 | | rock | 30.8 | 49.58 | | wardrobe | 44.62 | 58.87 | | lamp | 61.92 | 76.03 | | bathtub | 74.06 | 80.62 | | railing | 27.76 | 41.21 | | cushion | 52.64 | 65.57 | | base | 20.28 | 28.29 | | box | 21.62 | 27.47 | | column | 44.43 | 56.09 | | signboard | 36.47 | 48.88 | | chest of drawers | 36.99 | 54.57 | | counter | 24.69 | 28.54 | | sand | 30.67 | 49.25 | | sink | 66.4 | 78.81 | | skyscraper | 65.2 | 73.99 | | fireplace | 70.97 | 86.02 | | refrigerator | 71.05 | 84.9 | | grandstand | 39.8 | 66.01 | | path | 14.14 | 20.56 | | stairs | 30.04 | 36.38 | | runway | 58.7 | 76.87 | | case | 44.54 | 66.1 | | pool table | 91.25 | 95.82 | | pillow | 53.83 | 66.2 | | screen door | 64.3 | 72.71 | | stairway | 29.91 | 39.67 | | river | 12.41 | 23.61 | | bridge | 61.61 | 67.77 | | bookcase | 38.12 | 47.78 | | blind | 40.38 | 48.13 | | coffee table | 55.7 | 79.75 | | toilet | 84.57 | 91.47 | | flower | 34.66 | 45.8 | | book | 44.77 | 63.91 | | hill | 4.74 | 6.27 | | bench | 37.54 | 47.96 | | countertop | 55.46 | 71.56 | | stove | 72.69 | 80.46 | | palm | 51.03 | 70.36 | | kitchen island | 44.83 | 80.33 | | computer | 54.45 | 64.46 | | swivel chair | 44.64 | 61.34 | | boat | 51.32 | 63.23 | | bar | 24.88 | 30.82 | | arcade machine | 23.74 | 26.88 | | hovel | 36.23 | 38.58 | | bus | 78.16 | 86.42 | | towel | 55.32 | 65.55 | | light | 51.77 | 57.3 | | truck | 30.47 | 41.69 | | tower | 30.0 | 35.92 | | chandelier | 67.45 | 83.19 | | awning | 25.77 | 29.12 | | streetlight | 23.96 | 28.41 | | booth | 40.54 | 44.01 | | television receiver | 65.71 | 80.05 | | airplane | 50.62 | 63.89 | | dirt track | 2.47 | 7.15 | | apparel | 28.91 | 39.64 | | pole | 24.28 | 37.02 | | land | 0.61 | 0.87 | | bannister | 8.56 | 10.56 | | escalator | 21.46 | 22.22 | | ottoman | 43.15 | 53.79 | | bottle | 12.77 | 20.82 | | buffet | 34.42 | 42.51 | | poster | 25.32 | 33.91 | | stage | 10.57 | 12.85 | | van | 39.99 | 52.81 | | ship | 62.65 | 67.52 | | fountain | 0.28 | 0.28 | | conveyer belt | 65.43 | 86.09 | | canopy | 16.88 | 20.14 | | washer | 63.57 | 65.4 | | plaything | 21.48 | 26.77 | | swimming pool | 28.32 | 34.32 | | stool | 39.95 | 51.11 | | barrel | 33.78 | 64.85 | | basket | 23.44 | 32.64 | | waterfall | 57.11 | 81.57 | | tent | 91.77 | 98.33 | | bag | 9.29 | 11.35 | | minibike | 48.56 | 56.94 | | cradle | 75.67 | 98.19 | | oven | 20.95 | 50.4 | | ball | 41.99 | 67.46 | | food | 50.48 | 58.24 | | step | 5.35 | 6.78 | | tank | 44.95 | 45.94 | | trade name | 24.01 | 28.42 | | microwave | 39.31 | 42.8 | | pot | 38.02 | 45.4 | | animal | 51.99 | 54.18 | | bicycle | 45.42 | 73.32 | | lake | 61.23 | 63.02 | | dishwasher | 73.39 | 77.09 | | screen | 59.32 | 72.4 | | blanket | 7.04 | 8.42 | | sculpture | 40.78 | 62.06 | | hood | 59.38 | 69.64 | | sconce | 40.63 | 47.64 | | vase | 31.98 | 46.15 | | traffic light | 25.85 | 35.34 | | tray | 4.69 | 7.26 | | ashcan | 41.52 | 56.51 | | fan | 55.82 | 67.46 | | pier | 19.9 | 25.25 | | crt screen | 4.74 | 8.47 | | plate | 38.22 | 48.77 | | monitor | 63.32 | 74.55 | | bulletin board | 34.12 | 51.64 | | shower | 1.07 | 1.69 | | radiator | 41.45 | 50.3 | | glass | 11.12 | 12.71 | | clock | 20.87 | 27.01 | | flag | 39.81 | 43.57 | +---------------------+-------+-------+ 2023-03-03 17:27:12,976 - mmseg - INFO - Summary: 2023-03-03 17:27:12,976 - mmseg - INFO - +-------+-------+------+ | aAcc | mIoU | mAcc | +-------+-------+------+ | 81.47 | 44.57 | 55.5 | +-------+-------+------+ 2023-03-03 17:27:13,005 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_24000.pth was removed 2023-03-03 17:27:13,828 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_32000.pth. 2023-03-03 17:27:13,829 - mmseg - INFO - Best mIoU is 0.4457 at 32000 iter. 2023-03-03 17:27:13,829 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:27:13,829 - mmseg - INFO - Iter(val) [250] aAcc: 0.8147, mIoU: 0.4457, mAcc: 0.5550, IoU.background: nan, IoU.wall: 0.7560, IoU.building: 0.8239, IoU.sky: 0.9407, IoU.floor: 0.7901, IoU.tree: 0.7310, IoU.ceiling: 0.8217, IoU.road: 0.8072, IoU.bed : 0.8769, IoU.windowpane: 0.5961, IoU.grass: 0.6540, IoU.cabinet: 0.5815, IoU.sidewalk: 0.6474, IoU.person: 0.7779, IoU.earth: 0.3101, IoU.door: 0.4544, IoU.table: 0.5963, IoU.mountain: 0.5146, IoU.plant: 0.5045, IoU.curtain: 0.7147, IoU.chair: 0.5490, IoU.car: 0.8086, IoU.water: 0.4563, IoU.painting: 0.7217, IoU.sofa: 0.6116, IoU.shelf: 0.3836, IoU.house: 0.4646, IoU.sea: 0.4263, IoU.mirror: 0.6296, IoU.rug: 0.5544, IoU.field: 0.2280, IoU.armchair: 0.4092, IoU.seat: 0.5818, IoU.fence: 0.3366, IoU.desk: 0.4972, IoU.rock: 0.3080, IoU.wardrobe: 0.4462, IoU.lamp: 0.6192, IoU.bathtub: 0.7406, IoU.railing: 0.2776, IoU.cushion: 0.5264, IoU.base: 0.2028, IoU.box: 0.2162, IoU.column: 0.4443, IoU.signboard: 0.3647, IoU.chest of drawers: 0.3699, IoU.counter: 0.2469, IoU.sand: 0.3067, IoU.sink: 0.6640, IoU.skyscraper: 0.6520, IoU.fireplace: 0.7097, IoU.refrigerator: 0.7105, IoU.grandstand: 0.3980, IoU.path: 0.1414, IoU.stairs: 0.3004, IoU.runway: 0.5870, IoU.case: 0.4454, IoU.pool table: 0.9125, IoU.pillow: 0.5383, IoU.screen door: 0.6430, IoU.stairway: 0.2991, IoU.river: 0.1241, IoU.bridge: 0.6161, IoU.bookcase: 0.3812, IoU.blind: 0.4038, IoU.coffee table: 0.5570, IoU.toilet: 0.8457, IoU.flower: 0.3466, IoU.book: 0.4477, IoU.hill: 0.0474, IoU.bench: 0.3754, IoU.countertop: 0.5546, IoU.stove: 0.7269, IoU.palm: 0.5103, IoU.kitchen island: 0.4483, IoU.computer: 0.5445, IoU.swivel chair: 0.4464, IoU.boat: 0.5132, IoU.bar: 0.2488, IoU.arcade machine: 0.2374, IoU.hovel: 0.3623, IoU.bus: 0.7816, IoU.towel: 0.5532, IoU.light: 0.5177, IoU.truck: 0.3047, IoU.tower: 0.3000, IoU.chandelier: 0.6745, IoU.awning: 0.2577, IoU.streetlight: 0.2396, IoU.booth: 0.4054, IoU.television receiver: 0.6571, IoU.airplane: 0.5062, IoU.dirt track: 0.0247, IoU.apparel: 0.2891, IoU.pole: 0.2428, IoU.land: 0.0061, IoU.bannister: 0.0856, IoU.escalator: 0.2146, IoU.ottoman: 0.4315, IoU.bottle: 0.1277, IoU.buffet: 0.3442, IoU.poster: 0.2532, IoU.stage: 0.1057, IoU.van: 0.3999, IoU.ship: 0.6265, IoU.fountain: 0.0028, IoU.conveyer belt: 0.6543, IoU.canopy: 0.1688, IoU.washer: 0.6357, IoU.plaything: 0.2148, IoU.swimming pool: 0.2832, IoU.stool: 0.3995, IoU.barrel: 0.3378, IoU.basket: 0.2344, IoU.waterfall: 0.5711, IoU.tent: 0.9177, IoU.bag: 0.0929, IoU.minibike: 0.4856, IoU.cradle: 0.7567, IoU.oven: 0.2095, IoU.ball: 0.4199, IoU.food: 0.5048, IoU.step: 0.0535, IoU.tank: 0.4495, IoU.trade name: 0.2401, IoU.microwave: 0.3931, IoU.pot: 0.3802, IoU.animal: 0.5199, IoU.bicycle: 0.4542, IoU.lake: 0.6123, IoU.dishwasher: 0.7339, IoU.screen: 0.5932, IoU.blanket: 0.0704, IoU.sculpture: 0.4078, IoU.hood: 0.5938, IoU.sconce: 0.4063, IoU.vase: 0.3198, IoU.traffic light: 0.2585, IoU.tray: 0.0469, IoU.ashcan: 0.4152, IoU.fan: 0.5582, IoU.pier: 0.1990, IoU.crt screen: 0.0474, IoU.plate: 0.3822, IoU.monitor: 0.6332, IoU.bulletin board: 0.3412, IoU.shower: 0.0107, IoU.radiator: 0.4145, IoU.glass: 0.1112, IoU.clock: 0.2087, IoU.flag: 0.3981, Acc.background: nan, Acc.wall: 0.8831, Acc.building: 0.9314, Acc.sky: 0.9731, Acc.floor: 0.8971, Acc.tree: 0.8813, Acc.ceiling: 0.9171, Acc.road: 0.8931, Acc.bed : 0.9520, Acc.windowpane: 0.7588, Acc.grass: 0.8335, Acc.cabinet: 0.7248, Acc.sidewalk: 0.7975, Acc.person: 0.9083, Acc.earth: 0.4278, Acc.door: 0.5966, Acc.table: 0.7361, Acc.mountain: 0.6775, Acc.plant: 0.6296, Acc.curtain: 0.8275, Acc.chair: 0.7102, Acc.car: 0.9014, Acc.water: 0.6220, Acc.painting: 0.8422, Acc.sofa: 0.8309, Acc.shelf: 0.5491, Acc.house: 0.5633, Acc.sea: 0.6871, Acc.mirror: 0.7183, Acc.rug: 0.6405, Acc.field: 0.3559, Acc.armchair: 0.5404, Acc.seat: 0.7606, Acc.fence: 0.4680, Acc.desk: 0.6707, Acc.rock: 0.4958, Acc.wardrobe: 0.5887, Acc.lamp: 0.7603, Acc.bathtub: 0.8062, Acc.railing: 0.4121, Acc.cushion: 0.6557, Acc.base: 0.2829, Acc.box: 0.2747, Acc.column: 0.5609, Acc.signboard: 0.4888, Acc.chest of drawers: 0.5457, Acc.counter: 0.2854, Acc.sand: 0.4925, Acc.sink: 0.7881, Acc.skyscraper: 0.7399, Acc.fireplace: 0.8602, Acc.refrigerator: 0.8490, Acc.grandstand: 0.6601, Acc.path: 0.2056, Acc.stairs: 0.3638, Acc.runway: 0.7687, Acc.case: 0.6610, Acc.pool table: 0.9582, Acc.pillow: 0.6620, Acc.screen door: 0.7271, Acc.stairway: 0.3967, Acc.river: 0.2361, Acc.bridge: 0.6777, Acc.bookcase: 0.4778, Acc.blind: 0.4813, Acc.coffee table: 0.7975, Acc.toilet: 0.9147, Acc.flower: 0.4580, Acc.book: 0.6391, Acc.hill: 0.0627, Acc.bench: 0.4796, Acc.countertop: 0.7156, Acc.stove: 0.8046, Acc.palm: 0.7036, Acc.kitchen island: 0.8033, Acc.computer: 0.6446, Acc.swivel chair: 0.6134, Acc.boat: 0.6323, Acc.bar: 0.3082, Acc.arcade machine: 0.2688, Acc.hovel: 0.3858, Acc.bus: 0.8642, Acc.towel: 0.6555, Acc.light: 0.5730, Acc.truck: 0.4169, Acc.tower: 0.3592, Acc.chandelier: 0.8319, Acc.awning: 0.2912, Acc.streetlight: 0.2841, Acc.booth: 0.4401, Acc.television receiver: 0.8005, Acc.airplane: 0.6389, Acc.dirt track: 0.0715, Acc.apparel: 0.3964, Acc.pole: 0.3702, Acc.land: 0.0087, Acc.bannister: 0.1056, Acc.escalator: 0.2222, Acc.ottoman: 0.5379, Acc.bottle: 0.2082, Acc.buffet: 0.4251, Acc.poster: 0.3391, Acc.stage: 0.1285, Acc.van: 0.5281, Acc.ship: 0.6752, Acc.fountain: 0.0028, Acc.conveyer belt: 0.8609, Acc.canopy: 0.2014, Acc.washer: 0.6540, Acc.plaything: 0.2677, Acc.swimming pool: 0.3432, Acc.stool: 0.5111, Acc.barrel: 0.6485, Acc.basket: 0.3264, Acc.waterfall: 0.8157, Acc.tent: 0.9833, Acc.bag: 0.1135, Acc.minibike: 0.5694, Acc.cradle: 0.9819, Acc.oven: 0.5040, Acc.ball: 0.6746, Acc.food: 0.5824, Acc.step: 0.0678, Acc.tank: 0.4594, Acc.trade name: 0.2842, Acc.microwave: 0.4280, Acc.pot: 0.4540, Acc.animal: 0.5418, Acc.bicycle: 0.7332, Acc.lake: 0.6302, Acc.dishwasher: 0.7709, Acc.screen: 0.7240, Acc.blanket: 0.0842, Acc.sculpture: 0.6206, Acc.hood: 0.6964, Acc.sconce: 0.4764, Acc.vase: 0.4615, Acc.traffic light: 0.3534, Acc.tray: 0.0726, Acc.ashcan: 0.5651, Acc.fan: 0.6746, Acc.pier: 0.2525, Acc.crt screen: 0.0847, Acc.plate: 0.4877, Acc.monitor: 0.7455, Acc.bulletin board: 0.5164, Acc.shower: 0.0169, Acc.radiator: 0.5030, Acc.glass: 0.1271, Acc.clock: 0.2701, Acc.flag: 0.4357 2023-03-03 17:27:24,031 - mmseg - INFO - Iter [32050/80000] lr: 1.875e-05, eta: 3:07:32, time: 0.516, data_time: 0.319, memory: 67202, decode.loss_ce: 0.2268, decode.acc_seg: 91.0748, loss: 0.2268 2023-03-03 17:27:34,075 - mmseg - INFO - Iter [32100/80000] lr: 1.875e-05, eta: 3:07:18, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2196, decode.acc_seg: 91.3699, loss: 0.2196 2023-03-03 17:27:44,045 - mmseg - INFO - Iter [32150/80000] lr: 1.875e-05, eta: 3:07:03, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2116, decode.acc_seg: 91.5687, loss: 0.2116 2023-03-03 17:27:56,451 - mmseg - INFO - Iter [32200/80000] lr: 1.875e-05, eta: 3:06:53, time: 0.248, data_time: 0.052, memory: 67202, decode.loss_ce: 0.2179, decode.acc_seg: 91.2049, loss: 0.2179 2023-03-03 17:28:06,510 - mmseg - INFO - Iter [32250/80000] lr: 1.875e-05, eta: 3:06:38, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2238, decode.acc_seg: 91.1367, loss: 0.2238 2023-03-03 17:28:16,452 - mmseg - INFO - Iter [32300/80000] lr: 1.875e-05, eta: 3:06:24, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2190, decode.acc_seg: 91.3194, loss: 0.2190 2023-03-03 17:28:26,331 - mmseg - INFO - Iter [32350/80000] lr: 1.875e-05, eta: 3:06:10, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.4243, loss: 0.2143 2023-03-03 17:28:36,199 - mmseg - INFO - Iter [32400/80000] lr: 1.875e-05, eta: 3:05:55, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2186, decode.acc_seg: 91.2564, loss: 0.2186 2023-03-03 17:28:46,300 - mmseg - INFO - Iter [32450/80000] lr: 1.875e-05, eta: 3:05:41, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2069, decode.acc_seg: 91.7324, loss: 0.2069 2023-03-03 17:28:56,412 - mmseg - INFO - Iter [32500/80000] lr: 1.875e-05, eta: 3:05:27, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2196, decode.acc_seg: 91.3949, loss: 0.2196 2023-03-03 17:29:06,292 - mmseg - INFO - Iter [32550/80000] lr: 1.875e-05, eta: 3:05:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2184, decode.acc_seg: 91.3327, loss: 0.2184 2023-03-03 17:29:16,458 - mmseg - INFO - Iter [32600/80000] lr: 1.875e-05, eta: 3:04:59, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2096, decode.acc_seg: 91.6078, loss: 0.2096 2023-03-03 17:29:26,449 - mmseg - INFO - Iter [32650/80000] lr: 1.875e-05, eta: 3:04:44, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.4560, loss: 0.2143 2023-03-03 17:29:36,524 - mmseg - INFO - Iter [32700/80000] lr: 1.875e-05, eta: 3:04:30, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2112, decode.acc_seg: 91.5047, loss: 0.2112 2023-03-03 17:29:46,857 - mmseg - INFO - Iter [32750/80000] lr: 1.875e-05, eta: 3:04:17, time: 0.207, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2129, decode.acc_seg: 91.2918, loss: 0.2129 2023-03-03 17:29:56,901 - mmseg - INFO - Iter [32800/80000] lr: 1.875e-05, eta: 3:04:03, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2139, decode.acc_seg: 91.4345, loss: 0.2139 2023-03-03 17:30:09,390 - mmseg - INFO - Iter [32850/80000] lr: 1.875e-05, eta: 3:03:52, time: 0.250, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2165, decode.acc_seg: 91.4086, loss: 0.2165 2023-03-03 17:30:19,313 - mmseg - INFO - Iter [32900/80000] lr: 1.875e-05, eta: 3:03:38, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2146, decode.acc_seg: 91.3478, loss: 0.2146 2023-03-03 17:30:29,351 - mmseg - INFO - Iter [32950/80000] lr: 1.875e-05, eta: 3:03:24, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2120, decode.acc_seg: 91.5235, loss: 0.2120 2023-03-03 17:30:39,469 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:30:39,470 - mmseg - INFO - Iter [33000/80000] lr: 1.875e-05, eta: 3:03:10, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2157, decode.acc_seg: 91.4506, loss: 0.2157 2023-03-03 17:30:49,564 - mmseg - INFO - Iter [33050/80000] lr: 1.875e-05, eta: 3:02:56, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2137, decode.acc_seg: 91.6207, loss: 0.2137 2023-03-03 17:30:59,529 - mmseg - INFO - Iter [33100/80000] lr: 1.875e-05, eta: 3:02:42, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2160, decode.acc_seg: 91.3488, loss: 0.2160 2023-03-03 17:31:09,623 - mmseg - INFO - Iter [33150/80000] lr: 1.875e-05, eta: 3:02:28, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2190, decode.acc_seg: 91.2813, loss: 0.2190 2023-03-03 17:31:19,604 - mmseg - INFO - Iter [33200/80000] lr: 1.875e-05, eta: 3:02:14, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2213, decode.acc_seg: 91.2765, loss: 0.2213 2023-03-03 17:31:29,643 - mmseg - INFO - Iter [33250/80000] lr: 1.875e-05, eta: 3:02:00, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2120, decode.acc_seg: 91.2954, loss: 0.2120 2023-03-03 17:31:39,650 - mmseg - INFO - Iter [33300/80000] lr: 1.875e-05, eta: 3:01:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.6046, loss: 0.2080 2023-03-03 17:31:49,719 - mmseg - INFO - Iter [33350/80000] lr: 1.875e-05, eta: 3:01:32, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2094, decode.acc_seg: 91.4924, loss: 0.2094 2023-03-03 17:31:59,623 - mmseg - INFO - Iter [33400/80000] lr: 1.875e-05, eta: 3:01:18, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2187, decode.acc_seg: 91.4134, loss: 0.2187 2023-03-03 17:32:12,194 - mmseg - INFO - Iter [33450/80000] lr: 1.875e-05, eta: 3:01:07, time: 0.251, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2179, decode.acc_seg: 91.4413, loss: 0.2179 2023-03-03 17:32:22,123 - mmseg - INFO - Iter [33500/80000] lr: 1.875e-05, eta: 3:00:53, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.4410, loss: 0.2099 2023-03-03 17:32:32,603 - mmseg - INFO - Iter [33550/80000] lr: 1.875e-05, eta: 3:00:40, time: 0.210, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2215, decode.acc_seg: 91.1192, loss: 0.2215 2023-03-03 17:32:42,788 - mmseg - INFO - Iter [33600/80000] lr: 1.875e-05, eta: 3:00:26, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2188, decode.acc_seg: 91.2766, loss: 0.2188 2023-03-03 17:32:52,774 - mmseg - INFO - Iter [33650/80000] lr: 1.875e-05, eta: 3:00:12, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2167, decode.acc_seg: 91.4360, loss: 0.2167 2023-03-03 17:33:02,859 - mmseg - INFO - Iter [33700/80000] lr: 1.875e-05, eta: 2:59:58, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2102, decode.acc_seg: 91.4384, loss: 0.2102 2023-03-03 17:33:12,899 - mmseg - INFO - Iter [33750/80000] lr: 1.875e-05, eta: 2:59:44, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2209, decode.acc_seg: 90.9916, loss: 0.2209 2023-03-03 17:33:22,909 - mmseg - INFO - Iter [33800/80000] lr: 1.875e-05, eta: 2:59:30, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2137, decode.acc_seg: 91.4907, loss: 0.2137 2023-03-03 17:33:32,917 - mmseg - INFO - Iter [33850/80000] lr: 1.875e-05, eta: 2:59:16, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.4220, loss: 0.2125 2023-03-03 17:33:42,912 - mmseg - INFO - Iter [33900/80000] lr: 1.875e-05, eta: 2:59:03, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2188, decode.acc_seg: 91.3841, loss: 0.2188 2023-03-03 17:33:52,819 - mmseg - INFO - Iter [33950/80000] lr: 1.875e-05, eta: 2:58:49, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2095, decode.acc_seg: 91.6406, loss: 0.2095 2023-03-03 17:34:02,763 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:34:02,763 - mmseg - INFO - Iter [34000/80000] lr: 1.875e-05, eta: 2:58:35, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2139, decode.acc_seg: 91.4776, loss: 0.2139 2023-03-03 17:34:12,766 - mmseg - INFO - Iter [34050/80000] lr: 1.875e-05, eta: 2:58:21, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.6443, loss: 0.2081 2023-03-03 17:34:25,131 - mmseg - INFO - Iter [34100/80000] lr: 1.875e-05, eta: 2:58:10, time: 0.247, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.0619, loss: 0.2212 2023-03-03 17:34:35,278 - mmseg - INFO - Iter [34150/80000] lr: 1.875e-05, eta: 2:57:56, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.3030, loss: 0.2181 2023-03-03 17:34:45,254 - mmseg - INFO - Iter [34200/80000] lr: 1.875e-05, eta: 2:57:43, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2185, decode.acc_seg: 91.2743, loss: 0.2185 2023-03-03 17:34:55,738 - mmseg - INFO - Iter [34250/80000] lr: 1.875e-05, eta: 2:57:29, time: 0.210, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2174, decode.acc_seg: 91.2464, loss: 0.2174 2023-03-03 17:35:05,769 - mmseg - INFO - Iter [34300/80000] lr: 1.875e-05, eta: 2:57:16, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2069, decode.acc_seg: 91.6970, loss: 0.2069 2023-03-03 17:35:15,700 - mmseg - INFO - Iter [34350/80000] lr: 1.875e-05, eta: 2:57:02, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.6582, loss: 0.2144 2023-03-03 17:35:25,769 - mmseg - INFO - Iter [34400/80000] lr: 1.875e-05, eta: 2:56:48, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.5230, loss: 0.2144 2023-03-03 17:35:35,956 - mmseg - INFO - Iter [34450/80000] lr: 1.875e-05, eta: 2:56:34, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2183, decode.acc_seg: 91.1750, loss: 0.2183 2023-03-03 17:35:45,837 - mmseg - INFO - Iter [34500/80000] lr: 1.875e-05, eta: 2:56:20, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.4785, loss: 0.2128 2023-03-03 17:35:55,795 - mmseg - INFO - Iter [34550/80000] lr: 1.875e-05, eta: 2:56:07, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.2943, loss: 0.2212 2023-03-03 17:36:05,910 - mmseg - INFO - Iter [34600/80000] lr: 1.875e-05, eta: 2:55:53, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2280, decode.acc_seg: 91.0198, loss: 0.2280 2023-03-03 17:36:15,885 - mmseg - INFO - Iter [34650/80000] lr: 1.875e-05, eta: 2:55:39, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2193, decode.acc_seg: 91.3046, loss: 0.2193 2023-03-03 17:36:25,918 - mmseg - INFO - Iter [34700/80000] lr: 1.875e-05, eta: 2:55:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2058, decode.acc_seg: 91.8472, loss: 0.2058 2023-03-03 17:36:38,467 - mmseg - INFO - Iter [34750/80000] lr: 1.875e-05, eta: 2:55:15, time: 0.251, data_time: 0.058, memory: 67202, decode.loss_ce: 0.2244, decode.acc_seg: 91.1477, loss: 0.2244 2023-03-03 17:36:48,474 - mmseg - INFO - Iter [34800/80000] lr: 1.875e-05, eta: 2:55:01, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.5770, loss: 0.2122 2023-03-03 17:36:58,370 - mmseg - INFO - Iter [34850/80000] lr: 1.875e-05, eta: 2:54:48, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.4360, loss: 0.2150 2023-03-03 17:37:08,306 - mmseg - INFO - Iter [34900/80000] lr: 1.875e-05, eta: 2:54:34, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2166, decode.acc_seg: 91.3501, loss: 0.2166 2023-03-03 17:37:18,295 - mmseg - INFO - Iter [34950/80000] lr: 1.875e-05, eta: 2:54:20, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2219, decode.acc_seg: 91.1466, loss: 0.2219 2023-03-03 17:37:28,366 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:37:28,367 - mmseg - INFO - Iter [35000/80000] lr: 1.875e-05, eta: 2:54:07, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2177, decode.acc_seg: 91.4699, loss: 0.2177 2023-03-03 17:37:38,506 - mmseg - INFO - Iter [35050/80000] lr: 1.875e-05, eta: 2:53:53, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.5786, loss: 0.2124 2023-03-03 17:37:48,504 - mmseg - INFO - Iter [35100/80000] lr: 1.875e-05, eta: 2:53:39, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2161, decode.acc_seg: 91.3858, loss: 0.2161 2023-03-03 17:37:58,380 - mmseg - INFO - Iter [35150/80000] lr: 1.875e-05, eta: 2:53:26, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2236, decode.acc_seg: 91.0798, loss: 0.2236 2023-03-03 17:38:08,319 - mmseg - INFO - Iter [35200/80000] lr: 1.875e-05, eta: 2:53:12, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2184, decode.acc_seg: 91.3151, loss: 0.2184 2023-03-03 17:38:18,348 - mmseg - INFO - Iter [35250/80000] lr: 1.875e-05, eta: 2:52:58, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2186, decode.acc_seg: 91.2153, loss: 0.2186 2023-03-03 17:38:28,275 - mmseg - INFO - Iter [35300/80000] lr: 1.875e-05, eta: 2:52:45, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2131, decode.acc_seg: 91.3998, loss: 0.2131 2023-03-03 17:38:40,760 - mmseg - INFO - Iter [35350/80000] lr: 1.875e-05, eta: 2:52:34, time: 0.250, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2177, decode.acc_seg: 91.2891, loss: 0.2177 2023-03-03 17:38:50,909 - mmseg - INFO - Iter [35400/80000] lr: 1.875e-05, eta: 2:52:21, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2154, decode.acc_seg: 91.5115, loss: 0.2154 2023-03-03 17:39:00,937 - mmseg - INFO - Iter [35450/80000] lr: 1.875e-05, eta: 2:52:07, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2156, decode.acc_seg: 91.4839, loss: 0.2156 2023-03-03 17:39:11,104 - mmseg - INFO - Iter [35500/80000] lr: 1.875e-05, eta: 2:51:54, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.4433, loss: 0.2181 2023-03-03 17:39:21,026 - mmseg - INFO - Iter [35550/80000] lr: 1.875e-05, eta: 2:51:40, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.4117, loss: 0.2144 2023-03-03 17:39:31,252 - mmseg - INFO - Iter [35600/80000] lr: 1.875e-05, eta: 2:51:27, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2185, decode.acc_seg: 91.2939, loss: 0.2185 2023-03-03 17:39:41,112 - mmseg - INFO - Iter [35650/80000] lr: 1.875e-05, eta: 2:51:13, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.5757, loss: 0.2081 2023-03-03 17:39:51,001 - mmseg - INFO - Iter [35700/80000] lr: 1.875e-05, eta: 2:50:59, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2005, decode.acc_seg: 91.8519, loss: 0.2005 2023-03-03 17:40:01,022 - mmseg - INFO - Iter [35750/80000] lr: 1.875e-05, eta: 2:50:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2178, decode.acc_seg: 91.2550, loss: 0.2178 2023-03-03 17:40:11,045 - mmseg - INFO - Iter [35800/80000] lr: 1.875e-05, eta: 2:50:32, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2213, decode.acc_seg: 91.2212, loss: 0.2213 2023-03-03 17:40:20,898 - mmseg - INFO - Iter [35850/80000] lr: 1.875e-05, eta: 2:50:19, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2164, decode.acc_seg: 91.4850, loss: 0.2164 2023-03-03 17:40:30,881 - mmseg - INFO - Iter [35900/80000] lr: 1.875e-05, eta: 2:50:05, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2043, decode.acc_seg: 91.8599, loss: 0.2043 2023-03-03 17:40:41,116 - mmseg - INFO - Iter [35950/80000] lr: 1.875e-05, eta: 2:49:52, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2214, decode.acc_seg: 91.0370, loss: 0.2214 2023-03-03 17:40:53,741 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:40:53,741 - mmseg - INFO - Iter [36000/80000] lr: 1.875e-05, eta: 2:49:42, time: 0.253, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.6025, loss: 0.2128 2023-03-03 17:41:03,816 - mmseg - INFO - Iter [36050/80000] lr: 1.875e-05, eta: 2:49:28, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2189, decode.acc_seg: 91.1225, loss: 0.2189 2023-03-03 17:41:13,898 - mmseg - INFO - Iter [36100/80000] lr: 1.875e-05, eta: 2:49:15, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2244, decode.acc_seg: 91.0639, loss: 0.2244 2023-03-03 17:41:23,849 - mmseg - INFO - Iter [36150/80000] lr: 1.875e-05, eta: 2:49:01, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2136, decode.acc_seg: 91.5561, loss: 0.2136 2023-03-03 17:41:34,248 - mmseg - INFO - Iter [36200/80000] lr: 1.875e-05, eta: 2:48:48, time: 0.208, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2130, decode.acc_seg: 91.4764, loss: 0.2130 2023-03-03 17:41:44,439 - mmseg - INFO - Iter [36250/80000] lr: 1.875e-05, eta: 2:48:35, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.4225, loss: 0.2159 2023-03-03 17:41:54,503 - mmseg - INFO - Iter [36300/80000] lr: 1.875e-05, eta: 2:48:22, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.3014, loss: 0.2149 2023-03-03 17:42:04,561 - mmseg - INFO - Iter [36350/80000] lr: 1.875e-05, eta: 2:48:08, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2120, decode.acc_seg: 91.3522, loss: 0.2120 2023-03-03 17:42:14,437 - mmseg - INFO - Iter [36400/80000] lr: 1.875e-05, eta: 2:47:55, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2175, decode.acc_seg: 91.3393, loss: 0.2175 2023-03-03 17:42:24,669 - mmseg - INFO - Iter [36450/80000] lr: 1.875e-05, eta: 2:47:42, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2072, decode.acc_seg: 91.6104, loss: 0.2072 2023-03-03 17:42:34,902 - mmseg - INFO - Iter [36500/80000] lr: 1.875e-05, eta: 2:47:29, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2222, decode.acc_seg: 91.2260, loss: 0.2222 2023-03-03 17:42:44,841 - mmseg - INFO - Iter [36550/80000] lr: 1.875e-05, eta: 2:47:15, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2177, decode.acc_seg: 91.4462, loss: 0.2177 2023-03-03 17:42:57,482 - mmseg - INFO - Iter [36600/80000] lr: 1.875e-05, eta: 2:47:05, time: 0.253, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2168, decode.acc_seg: 91.3712, loss: 0.2168 2023-03-03 17:43:07,617 - mmseg - INFO - Iter [36650/80000] lr: 1.875e-05, eta: 2:46:52, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2179, decode.acc_seg: 91.2985, loss: 0.2179 2023-03-03 17:43:17,528 - mmseg - INFO - Iter [36700/80000] lr: 1.875e-05, eta: 2:46:38, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2116, decode.acc_seg: 91.4884, loss: 0.2116 2023-03-03 17:43:27,633 - mmseg - INFO - Iter [36750/80000] lr: 1.875e-05, eta: 2:46:25, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2190, decode.acc_seg: 91.2300, loss: 0.2190 2023-03-03 17:43:37,575 - mmseg - INFO - Iter [36800/80000] lr: 1.875e-05, eta: 2:46:11, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2161, decode.acc_seg: 91.4178, loss: 0.2161 2023-03-03 17:43:47,473 - mmseg - INFO - Iter [36850/80000] lr: 1.875e-05, eta: 2:45:58, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2084, decode.acc_seg: 91.6120, loss: 0.2084 2023-03-03 17:43:57,459 - mmseg - INFO - Iter [36900/80000] lr: 1.875e-05, eta: 2:45:45, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2129, decode.acc_seg: 91.6275, loss: 0.2129 2023-03-03 17:44:07,422 - mmseg - INFO - Iter [36950/80000] lr: 1.875e-05, eta: 2:45:31, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2197, decode.acc_seg: 91.4557, loss: 0.2197 2023-03-03 17:44:17,352 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:44:17,352 - mmseg - INFO - Iter [37000/80000] lr: 1.875e-05, eta: 2:45:18, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.5032, loss: 0.2090 2023-03-03 17:44:27,347 - mmseg - INFO - Iter [37050/80000] lr: 1.875e-05, eta: 2:45:05, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2192, decode.acc_seg: 91.2867, loss: 0.2192 2023-03-03 17:44:37,236 - mmseg - INFO - Iter [37100/80000] lr: 1.875e-05, eta: 2:44:51, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.5480, loss: 0.2110 2023-03-03 17:44:47,111 - mmseg - INFO - Iter [37150/80000] lr: 1.875e-05, eta: 2:44:38, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2164, decode.acc_seg: 91.3584, loss: 0.2164 2023-03-03 17:44:57,171 - mmseg - INFO - Iter [37200/80000] lr: 1.875e-05, eta: 2:44:24, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2191, decode.acc_seg: 91.1119, loss: 0.2191 2023-03-03 17:45:09,576 - mmseg - INFO - Iter [37250/80000] lr: 1.875e-05, eta: 2:44:14, time: 0.248, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.6154, loss: 0.2086 2023-03-03 17:45:19,563 - mmseg - INFO - Iter [37300/80000] lr: 1.875e-05, eta: 2:44:01, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.6085, loss: 0.2105 2023-03-03 17:45:29,554 - mmseg - INFO - Iter [37350/80000] lr: 1.875e-05, eta: 2:43:47, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2071, decode.acc_seg: 91.6714, loss: 0.2071 2023-03-03 17:45:39,523 - mmseg - INFO - Iter [37400/80000] lr: 1.875e-05, eta: 2:43:34, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2054, decode.acc_seg: 91.6490, loss: 0.2054 2023-03-03 17:45:49,519 - mmseg - INFO - Iter [37450/80000] lr: 1.875e-05, eta: 2:43:21, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2148, decode.acc_seg: 91.3549, loss: 0.2148 2023-03-03 17:45:59,535 - mmseg - INFO - Iter [37500/80000] lr: 1.875e-05, eta: 2:43:08, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2226, decode.acc_seg: 91.0993, loss: 0.2226 2023-03-03 17:46:09,765 - mmseg - INFO - Iter [37550/80000] lr: 1.875e-05, eta: 2:42:55, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2133, decode.acc_seg: 91.3635, loss: 0.2133 2023-03-03 17:46:19,751 - mmseg - INFO - Iter [37600/80000] lr: 1.875e-05, eta: 2:42:41, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2106, decode.acc_seg: 91.5601, loss: 0.2106 2023-03-03 17:46:29,794 - mmseg - INFO - Iter [37650/80000] lr: 1.875e-05, eta: 2:42:28, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2185, decode.acc_seg: 91.3360, loss: 0.2185 2023-03-03 17:46:39,685 - mmseg - INFO - Iter [37700/80000] lr: 1.875e-05, eta: 2:42:15, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2162, decode.acc_seg: 91.4019, loss: 0.2162 2023-03-03 17:46:49,735 - mmseg - INFO - Iter [37750/80000] lr: 1.875e-05, eta: 2:42:02, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2164, decode.acc_seg: 91.4815, loss: 0.2164 2023-03-03 17:46:59,864 - mmseg - INFO - Iter [37800/80000] lr: 1.875e-05, eta: 2:41:49, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2059, decode.acc_seg: 91.7971, loss: 0.2059 2023-03-03 17:47:09,809 - mmseg - INFO - Iter [37850/80000] lr: 1.875e-05, eta: 2:41:36, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.6564, loss: 0.2090 2023-03-03 17:47:22,337 - mmseg - INFO - Iter [37900/80000] lr: 1.875e-05, eta: 2:41:25, time: 0.251, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2156, decode.acc_seg: 91.3265, loss: 0.2156 2023-03-03 17:47:32,331 - mmseg - INFO - Iter [37950/80000] lr: 1.875e-05, eta: 2:41:12, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.6937, loss: 0.2060 2023-03-03 17:47:42,291 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:47:42,292 - mmseg - INFO - Iter [38000/80000] lr: 1.875e-05, eta: 2:40:59, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.8174, loss: 0.2060 2023-03-03 17:47:52,346 - mmseg - INFO - Iter [38050/80000] lr: 1.875e-05, eta: 2:40:46, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2185, decode.acc_seg: 91.4484, loss: 0.2185 2023-03-03 17:48:02,452 - mmseg - INFO - Iter [38100/80000] lr: 1.875e-05, eta: 2:40:33, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2131, decode.acc_seg: 91.4639, loss: 0.2131 2023-03-03 17:48:12,474 - mmseg - INFO - Iter [38150/80000] lr: 1.875e-05, eta: 2:40:20, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2133, decode.acc_seg: 91.5665, loss: 0.2133 2023-03-03 17:48:22,513 - mmseg - INFO - Iter [38200/80000] lr: 1.875e-05, eta: 2:40:06, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2097, decode.acc_seg: 91.5692, loss: 0.2097 2023-03-03 17:48:32,491 - mmseg - INFO - Iter [38250/80000] lr: 1.875e-05, eta: 2:39:53, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.5216, loss: 0.2149 2023-03-03 17:48:42,491 - mmseg - INFO - Iter [38300/80000] lr: 1.875e-05, eta: 2:39:40, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.4511, loss: 0.2127 2023-03-03 17:48:52,501 - mmseg - INFO - Iter [38350/80000] lr: 1.875e-05, eta: 2:39:27, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.4951, loss: 0.2091 2023-03-03 17:49:02,498 - mmseg - INFO - Iter [38400/80000] lr: 1.875e-05, eta: 2:39:14, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.4128, loss: 0.2149 2023-03-03 17:49:12,511 - mmseg - INFO - Iter [38450/80000] lr: 1.875e-05, eta: 2:39:01, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2194, decode.acc_seg: 91.2260, loss: 0.2194 2023-03-03 17:49:24,969 - mmseg - INFO - Iter [38500/80000] lr: 1.875e-05, eta: 2:38:51, time: 0.249, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2216, decode.acc_seg: 91.3354, loss: 0.2216 2023-03-03 17:49:34,882 - mmseg - INFO - Iter [38550/80000] lr: 1.875e-05, eta: 2:38:37, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2136, decode.acc_seg: 91.4290, loss: 0.2136 2023-03-03 17:49:44,790 - mmseg - INFO - Iter [38600/80000] lr: 1.875e-05, eta: 2:38:24, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2155, decode.acc_seg: 91.4055, loss: 0.2155 2023-03-03 17:49:54,859 - mmseg - INFO - Iter [38650/80000] lr: 1.875e-05, eta: 2:38:11, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.5394, loss: 0.2143 2023-03-03 17:50:05,087 - mmseg - INFO - Iter [38700/80000] lr: 1.875e-05, eta: 2:37:58, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2185, decode.acc_seg: 91.4555, loss: 0.2185 2023-03-03 17:50:15,087 - mmseg - INFO - Iter [38750/80000] lr: 1.875e-05, eta: 2:37:45, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2116, decode.acc_seg: 91.5536, loss: 0.2116 2023-03-03 17:50:25,205 - mmseg - INFO - Iter [38800/80000] lr: 1.875e-05, eta: 2:37:32, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2194, decode.acc_seg: 91.1657, loss: 0.2194 2023-03-03 17:50:35,200 - mmseg - INFO - Iter [38850/80000] lr: 1.875e-05, eta: 2:37:19, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2196, decode.acc_seg: 91.2127, loss: 0.2196 2023-03-03 17:50:45,223 - mmseg - INFO - Iter [38900/80000] lr: 1.875e-05, eta: 2:37:06, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2118, decode.acc_seg: 91.5660, loss: 0.2118 2023-03-03 17:50:55,238 - mmseg - INFO - Iter [38950/80000] lr: 1.875e-05, eta: 2:36:53, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.3888, loss: 0.2142 2023-03-03 17:51:05,178 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:51:05,178 - mmseg - INFO - Iter [39000/80000] lr: 1.875e-05, eta: 2:36:40, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.4398, loss: 0.2145 2023-03-03 17:51:15,116 - mmseg - INFO - Iter [39050/80000] lr: 1.875e-05, eta: 2:36:27, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2163, decode.acc_seg: 91.3400, loss: 0.2163 2023-03-03 17:51:25,216 - mmseg - INFO - Iter [39100/80000] lr: 1.875e-05, eta: 2:36:14, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2171, decode.acc_seg: 91.2396, loss: 0.2171 2023-03-03 17:51:37,699 - mmseg - INFO - Iter [39150/80000] lr: 1.875e-05, eta: 2:36:04, time: 0.250, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2087, decode.acc_seg: 91.6895, loss: 0.2087 2023-03-03 17:51:47,661 - mmseg - INFO - Iter [39200/80000] lr: 1.875e-05, eta: 2:35:51, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2120, decode.acc_seg: 91.5916, loss: 0.2120 2023-03-03 17:51:57,669 - mmseg - INFO - Iter [39250/80000] lr: 1.875e-05, eta: 2:35:38, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2118, decode.acc_seg: 91.5442, loss: 0.2118 2023-03-03 17:52:07,669 - mmseg - INFO - Iter [39300/80000] lr: 1.875e-05, eta: 2:35:25, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2205, decode.acc_seg: 91.4654, loss: 0.2205 2023-03-03 17:52:17,613 - mmseg - INFO - Iter [39350/80000] lr: 1.875e-05, eta: 2:35:12, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.6087, loss: 0.2115 2023-03-03 17:52:27,573 - mmseg - INFO - Iter [39400/80000] lr: 1.875e-05, eta: 2:34:59, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.5324, loss: 0.2143 2023-03-03 17:52:37,675 - mmseg - INFO - Iter [39450/80000] lr: 1.875e-05, eta: 2:34:46, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.2526, loss: 0.2135 2023-03-03 17:52:47,561 - mmseg - INFO - Iter [39500/80000] lr: 1.875e-05, eta: 2:34:33, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2106, decode.acc_seg: 91.5529, loss: 0.2106 2023-03-03 17:52:57,501 - mmseg - INFO - Iter [39550/80000] lr: 1.875e-05, eta: 2:34:20, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2245, decode.acc_seg: 91.2319, loss: 0.2245 2023-03-03 17:53:07,417 - mmseg - INFO - Iter [39600/80000] lr: 1.875e-05, eta: 2:34:07, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.2394, loss: 0.2212 2023-03-03 17:53:17,288 - mmseg - INFO - Iter [39650/80000] lr: 1.875e-05, eta: 2:33:54, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.4098, loss: 0.2117 2023-03-03 17:53:27,280 - mmseg - INFO - Iter [39700/80000] lr: 1.875e-05, eta: 2:33:41, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.4720, loss: 0.2150 2023-03-03 17:53:37,365 - mmseg - INFO - Iter [39750/80000] lr: 1.875e-05, eta: 2:33:28, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.4122, loss: 0.2099 2023-03-03 17:53:49,993 - mmseg - INFO - Iter [39800/80000] lr: 1.875e-05, eta: 2:33:18, time: 0.253, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2165, decode.acc_seg: 91.4650, loss: 0.2165 2023-03-03 17:54:00,103 - mmseg - INFO - Iter [39850/80000] lr: 1.875e-05, eta: 2:33:05, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2112, decode.acc_seg: 91.3865, loss: 0.2112 2023-03-03 17:54:10,207 - mmseg - INFO - Iter [39900/80000] lr: 1.875e-05, eta: 2:32:53, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2186, decode.acc_seg: 91.2749, loss: 0.2186 2023-03-03 17:54:20,444 - mmseg - INFO - Iter [39950/80000] lr: 1.875e-05, eta: 2:32:40, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2186, decode.acc_seg: 91.2221, loss: 0.2186 2023-03-03 17:54:30,453 - mmseg - INFO - Saving checkpoint at 40000 iterations 2023-03-03 17:54:31,335 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:54:31,335 - mmseg - INFO - Iter [40000/80000] lr: 1.875e-05, eta: 2:32:28, time: 0.218, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2130, decode.acc_seg: 91.4934, loss: 0.2130 2023-03-03 17:54:46,174 - mmseg - INFO - per class results: 2023-03-03 17:54:46,180 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 75.56 | 88.21 | | building | 82.41 | 93.49 | | sky | 94.12 | 97.15 | | floor | 78.69 | 90.73 | | tree | 73.3 | 88.09 | | ceiling | 82.17 | 91.52 | | road | 80.49 | 87.76 | | bed | 87.53 | 95.26 | | windowpane | 59.35 | 77.0 | | grass | 65.17 | 85.34 | | cabinet | 57.86 | 71.19 | | sidewalk | 63.43 | 81.62 | | person | 77.79 | 90.98 | | earth | 31.37 | 43.27 | | door | 46.03 | 61.32 | | table | 59.89 | 75.15 | | mountain | 51.13 | 66.8 | | plant | 50.7 | 63.76 | | curtain | 71.15 | 82.5 | | chair | 55.0 | 69.74 | | car | 80.98 | 90.62 | | water | 45.71 | 59.5 | | painting | 71.4 | 84.33 | | sofa | 62.76 | 82.13 | | shelf | 38.58 | 55.93 | | house | 45.98 | 54.25 | | sea | 44.15 | 72.41 | | mirror | 63.22 | 71.57 | | rug | 54.36 | 59.17 | | field | 21.52 | 32.16 | | armchair | 41.08 | 56.86 | | seat | 57.88 | 77.79 | | fence | 34.3 | 46.16 | | desk | 49.02 | 66.58 | | rock | 28.17 | 44.44 | | wardrobe | 44.27 | 62.14 | | lamp | 61.83 | 75.81 | | bathtub | 72.11 | 77.5 | | railing | 27.95 | 42.15 | | cushion | 52.55 | 63.85 | | base | 19.07 | 26.22 | | box | 22.1 | 28.41 | | column | 45.15 | 58.12 | | signboard | 34.55 | 48.98 | | chest of drawers | 37.32 | 57.01 | | counter | 27.21 | 33.25 | | sand | 31.94 | 47.57 | | sink | 66.26 | 79.43 | | skyscraper | 64.66 | 72.77 | | fireplace | 70.59 | 86.18 | | refrigerator | 71.67 | 83.2 | | grandstand | 40.02 | 61.26 | | path | 13.84 | 19.48 | | stairs | 29.96 | 36.18 | | runway | 60.61 | 80.23 | | case | 43.86 | 65.31 | | pool table | 91.49 | 95.73 | | pillow | 53.72 | 65.16 | | screen door | 64.85 | 70.4 | | stairway | 31.0 | 40.16 | | river | 12.09 | 22.41 | | bridge | 64.49 | 70.23 | | bookcase | 38.66 | 49.05 | | blind | 39.18 | 44.2 | | coffee table | 57.67 | 78.12 | | toilet | 85.55 | 90.25 | | flower | 34.95 | 45.9 | | book | 43.87 | 63.65 | | hill | 4.95 | 6.78 | | bench | 36.54 | 50.18 | | countertop | 53.52 | 67.77 | | stove | 72.28 | 81.04 | | palm | 51.2 | 71.54 | | kitchen island | 44.67 | 75.01 | | computer | 54.57 | 62.74 | | swivel chair | 44.62 | 59.43 | | boat | 47.29 | 57.73 | | bar | 25.24 | 31.16 | | arcade machine | 25.47 | 28.11 | | hovel | 33.18 | 34.97 | | bus | 78.18 | 87.19 | | towel | 55.07 | 63.43 | | light | 54.4 | 62.91 | | truck | 28.6 | 36.0 | | tower | 32.62 | 38.97 | | chandelier | 67.83 | 81.89 | | awning | 24.23 | 26.82 | | streetlight | 26.12 | 32.41 | | booth | 38.81 | 41.98 | | television receiver | 67.71 | 76.91 | | airplane | 48.51 | 61.24 | | dirt track | 2.77 | 6.09 | | apparel | 29.19 | 38.42 | | pole | 23.96 | 36.57 | | land | 0.68 | 1.0 | | bannister | 11.49 | 15.27 | | escalator | 20.9 | 21.67 | | ottoman | 41.59 | 53.15 | | bottle | 11.38 | 17.17 | | buffet | 34.34 | 43.24 | | poster | 24.49 | 31.2 | | stage | 9.85 | 12.15 | | van | 39.71 | 51.6 | | ship | 64.64 | 72.51 | | fountain | 0.57 | 0.57 | | conveyer belt | 59.08 | 87.47 | | canopy | 14.36 | 16.18 | | washer | 62.6 | 64.77 | | plaything | 22.19 | 29.2 | | swimming pool | 34.73 | 42.66 | | stool | 39.67 | 49.9 | | barrel | 42.16 | 64.81 | | basket | 22.12 | 32.56 | | waterfall | 60.85 | 82.44 | | tent | 93.58 | 98.04 | | bag | 7.6 | 8.59 | | minibike | 47.59 | 53.52 | | cradle | 76.62 | 96.32 | | oven | 23.61 | 53.07 | | ball | 45.35 | 66.82 | | food | 46.69 | 52.35 | | step | 3.06 | 3.42 | | tank | 39.95 | 40.26 | | trade name | 18.62 | 20.58 | | microwave | 38.44 | 40.98 | | pot | 35.6 | 40.59 | | animal | 46.81 | 47.81 | | bicycle | 44.9 | 67.03 | | lake | 60.36 | 63.05 | | dishwasher | 69.52 | 72.7 | | screen | 60.06 | 69.41 | | blanket | 7.49 | 8.95 | | sculpture | 38.68 | 62.01 | | hood | 58.85 | 69.86 | | sconce | 39.48 | 45.1 | | vase | 31.37 | 45.11 | | traffic light | 28.0 | 41.92 | | tray | 3.61 | 5.55 | | ashcan | 41.5 | 56.56 | | fan | 54.74 | 65.11 | | pier | 22.33 | 29.29 | | crt screen | 5.37 | 10.49 | | plate | 37.61 | 47.76 | | monitor | 60.65 | 70.25 | | bulletin board | 31.79 | 42.21 | | shower | 1.67 | 4.05 | | radiator | 41.54 | 48.46 | | glass | 9.63 | 10.46 | | clock | 19.72 | 24.53 | | flag | 38.45 | 40.78 | +---------------------+-------+-------+ 2023-03-03 17:54:46,180 - mmseg - INFO - Summary: 2023-03-03 17:54:46,181 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.44 | 44.38 | 54.83 | +-------+-------+-------+ 2023-03-03 17:54:46,181 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:54:46,182 - mmseg - INFO - Iter(val) [250] aAcc: 0.8144, mIoU: 0.4438, mAcc: 0.5483, IoU.background: nan, IoU.wall: 0.7556, IoU.building: 0.8241, IoU.sky: 0.9412, IoU.floor: 0.7869, IoU.tree: 0.7330, IoU.ceiling: 0.8217, IoU.road: 0.8049, IoU.bed : 0.8753, IoU.windowpane: 0.5935, IoU.grass: 0.6517, IoU.cabinet: 0.5786, IoU.sidewalk: 0.6343, IoU.person: 0.7779, IoU.earth: 0.3137, IoU.door: 0.4603, IoU.table: 0.5989, IoU.mountain: 0.5113, IoU.plant: 0.5070, IoU.curtain: 0.7115, IoU.chair: 0.5500, IoU.car: 0.8098, IoU.water: 0.4571, IoU.painting: 0.7140, IoU.sofa: 0.6276, IoU.shelf: 0.3858, IoU.house: 0.4598, IoU.sea: 0.4415, IoU.mirror: 0.6322, IoU.rug: 0.5436, IoU.field: 0.2152, IoU.armchair: 0.4108, IoU.seat: 0.5788, IoU.fence: 0.3430, IoU.desk: 0.4902, IoU.rock: 0.2817, IoU.wardrobe: 0.4427, IoU.lamp: 0.6183, IoU.bathtub: 0.7211, IoU.railing: 0.2795, IoU.cushion: 0.5255, IoU.base: 0.1907, IoU.box: 0.2210, IoU.column: 0.4515, IoU.signboard: 0.3455, IoU.chest of drawers: 0.3732, IoU.counter: 0.2721, IoU.sand: 0.3194, IoU.sink: 0.6626, IoU.skyscraper: 0.6466, IoU.fireplace: 0.7059, IoU.refrigerator: 0.7167, IoU.grandstand: 0.4002, IoU.path: 0.1384, IoU.stairs: 0.2996, IoU.runway: 0.6061, IoU.case: 0.4386, IoU.pool table: 0.9149, IoU.pillow: 0.5372, IoU.screen door: 0.6485, IoU.stairway: 0.3100, IoU.river: 0.1209, IoU.bridge: 0.6449, IoU.bookcase: 0.3866, IoU.blind: 0.3918, IoU.coffee table: 0.5767, IoU.toilet: 0.8555, IoU.flower: 0.3495, IoU.book: 0.4387, IoU.hill: 0.0495, IoU.bench: 0.3654, IoU.countertop: 0.5352, IoU.stove: 0.7228, IoU.palm: 0.5120, IoU.kitchen island: 0.4467, IoU.computer: 0.5457, IoU.swivel chair: 0.4462, IoU.boat: 0.4729, IoU.bar: 0.2524, IoU.arcade machine: 0.2547, IoU.hovel: 0.3318, IoU.bus: 0.7818, IoU.towel: 0.5507, IoU.light: 0.5440, IoU.truck: 0.2860, IoU.tower: 0.3262, IoU.chandelier: 0.6783, IoU.awning: 0.2423, IoU.streetlight: 0.2612, IoU.booth: 0.3881, IoU.television receiver: 0.6771, IoU.airplane: 0.4851, IoU.dirt track: 0.0277, IoU.apparel: 0.2919, IoU.pole: 0.2396, IoU.land: 0.0068, IoU.bannister: 0.1149, IoU.escalator: 0.2090, IoU.ottoman: 0.4159, IoU.bottle: 0.1138, IoU.buffet: 0.3434, IoU.poster: 0.2449, IoU.stage: 0.0985, IoU.van: 0.3971, IoU.ship: 0.6464, IoU.fountain: 0.0057, IoU.conveyer belt: 0.5908, IoU.canopy: 0.1436, IoU.washer: 0.6260, IoU.plaything: 0.2219, IoU.swimming pool: 0.3473, IoU.stool: 0.3967, IoU.barrel: 0.4216, IoU.basket: 0.2212, IoU.waterfall: 0.6085, IoU.tent: 0.9358, IoU.bag: 0.0760, IoU.minibike: 0.4759, IoU.cradle: 0.7662, IoU.oven: 0.2361, IoU.ball: 0.4535, IoU.food: 0.4669, IoU.step: 0.0306, IoU.tank: 0.3995, IoU.trade name: 0.1862, IoU.microwave: 0.3844, IoU.pot: 0.3560, IoU.animal: 0.4681, IoU.bicycle: 0.4490, IoU.lake: 0.6036, IoU.dishwasher: 0.6952, IoU.screen: 0.6006, IoU.blanket: 0.0749, IoU.sculpture: 0.3868, IoU.hood: 0.5885, IoU.sconce: 0.3948, IoU.vase: 0.3137, IoU.traffic light: 0.2800, IoU.tray: 0.0361, IoU.ashcan: 0.4150, IoU.fan: 0.5474, IoU.pier: 0.2233, IoU.crt screen: 0.0537, IoU.plate: 0.3761, IoU.monitor: 0.6065, IoU.bulletin board: 0.3179, IoU.shower: 0.0167, IoU.radiator: 0.4154, IoU.glass: 0.0963, IoU.clock: 0.1972, IoU.flag: 0.3845, Acc.background: nan, Acc.wall: 0.8821, Acc.building: 0.9349, Acc.sky: 0.9715, Acc.floor: 0.9073, Acc.tree: 0.8809, Acc.ceiling: 0.9152, Acc.road: 0.8776, Acc.bed : 0.9526, Acc.windowpane: 0.7700, Acc.grass: 0.8534, Acc.cabinet: 0.7119, Acc.sidewalk: 0.8162, Acc.person: 0.9098, Acc.earth: 0.4327, Acc.door: 0.6132, Acc.table: 0.7515, Acc.mountain: 0.6680, Acc.plant: 0.6376, Acc.curtain: 0.8250, Acc.chair: 0.6974, Acc.car: 0.9062, Acc.water: 0.5950, Acc.painting: 0.8433, Acc.sofa: 0.8213, Acc.shelf: 0.5593, Acc.house: 0.5425, Acc.sea: 0.7241, Acc.mirror: 0.7157, Acc.rug: 0.5917, Acc.field: 0.3216, Acc.armchair: 0.5686, Acc.seat: 0.7779, Acc.fence: 0.4616, Acc.desk: 0.6658, Acc.rock: 0.4444, Acc.wardrobe: 0.6214, Acc.lamp: 0.7581, Acc.bathtub: 0.7750, Acc.railing: 0.4215, Acc.cushion: 0.6385, Acc.base: 0.2622, Acc.box: 0.2841, Acc.column: 0.5812, Acc.signboard: 0.4898, Acc.chest of drawers: 0.5701, Acc.counter: 0.3325, Acc.sand: 0.4757, Acc.sink: 0.7943, Acc.skyscraper: 0.7277, Acc.fireplace: 0.8618, Acc.refrigerator: 0.8320, Acc.grandstand: 0.6126, Acc.path: 0.1948, Acc.stairs: 0.3618, Acc.runway: 0.8023, Acc.case: 0.6531, Acc.pool table: 0.9573, Acc.pillow: 0.6516, Acc.screen door: 0.7040, Acc.stairway: 0.4016, Acc.river: 0.2241, Acc.bridge: 0.7023, Acc.bookcase: 0.4905, Acc.blind: 0.4420, Acc.coffee table: 0.7812, Acc.toilet: 0.9025, Acc.flower: 0.4590, Acc.book: 0.6365, Acc.hill: 0.0678, Acc.bench: 0.5018, Acc.countertop: 0.6777, Acc.stove: 0.8104, Acc.palm: 0.7154, Acc.kitchen island: 0.7501, Acc.computer: 0.6274, Acc.swivel chair: 0.5943, Acc.boat: 0.5773, Acc.bar: 0.3116, Acc.arcade machine: 0.2811, Acc.hovel: 0.3497, Acc.bus: 0.8719, Acc.towel: 0.6343, Acc.light: 0.6291, Acc.truck: 0.3600, Acc.tower: 0.3897, Acc.chandelier: 0.8189, Acc.awning: 0.2682, Acc.streetlight: 0.3241, Acc.booth: 0.4198, Acc.television receiver: 0.7691, Acc.airplane: 0.6124, Acc.dirt track: 0.0609, Acc.apparel: 0.3842, Acc.pole: 0.3657, Acc.land: 0.0100, Acc.bannister: 0.1527, Acc.escalator: 0.2167, Acc.ottoman: 0.5315, Acc.bottle: 0.1717, Acc.buffet: 0.4324, Acc.poster: 0.3120, Acc.stage: 0.1215, Acc.van: 0.5160, Acc.ship: 0.7251, Acc.fountain: 0.0057, Acc.conveyer belt: 0.8747, Acc.canopy: 0.1618, Acc.washer: 0.6477, Acc.plaything: 0.2920, Acc.swimming pool: 0.4266, Acc.stool: 0.4990, Acc.barrel: 0.6481, Acc.basket: 0.3256, Acc.waterfall: 0.8244, Acc.tent: 0.9804, Acc.bag: 0.0859, Acc.minibike: 0.5352, Acc.cradle: 0.9632, Acc.oven: 0.5307, Acc.ball: 0.6682, Acc.food: 0.5235, Acc.step: 0.0342, Acc.tank: 0.4026, Acc.trade name: 0.2058, Acc.microwave: 0.4098, Acc.pot: 0.4059, Acc.animal: 0.4781, Acc.bicycle: 0.6703, Acc.lake: 0.6305, Acc.dishwasher: 0.7270, Acc.screen: 0.6941, Acc.blanket: 0.0895, Acc.sculpture: 0.6201, Acc.hood: 0.6986, Acc.sconce: 0.4510, Acc.vase: 0.4511, Acc.traffic light: 0.4192, Acc.tray: 0.0555, Acc.ashcan: 0.5656, Acc.fan: 0.6511, Acc.pier: 0.2929, Acc.crt screen: 0.1049, Acc.plate: 0.4776, Acc.monitor: 0.7025, Acc.bulletin board: 0.4221, Acc.shower: 0.0405, Acc.radiator: 0.4846, Acc.glass: 0.1046, Acc.clock: 0.2453, Acc.flag: 0.4078 2023-03-03 17:54:56,528 - mmseg - INFO - Iter [40050/80000] lr: 9.375e-06, eta: 2:32:30, time: 0.504, data_time: 0.304, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.3022, loss: 0.2143 2023-03-03 17:55:06,797 - mmseg - INFO - Iter [40100/80000] lr: 9.375e-06, eta: 2:32:18, time: 0.205, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2242, decode.acc_seg: 91.1454, loss: 0.2242 2023-03-03 17:55:16,906 - mmseg - INFO - Iter [40150/80000] lr: 9.375e-06, eta: 2:32:05, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.3946, loss: 0.2159 2023-03-03 17:55:26,894 - mmseg - INFO - Iter [40200/80000] lr: 9.375e-06, eta: 2:31:52, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2133, decode.acc_seg: 91.5709, loss: 0.2133 2023-03-03 17:55:36,775 - mmseg - INFO - Iter [40250/80000] lr: 9.375e-06, eta: 2:31:39, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2065, decode.acc_seg: 91.7379, loss: 0.2065 2023-03-03 17:55:46,811 - mmseg - INFO - Iter [40300/80000] lr: 9.375e-06, eta: 2:31:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2225, decode.acc_seg: 91.1577, loss: 0.2225 2023-03-03 17:55:56,801 - mmseg - INFO - Iter [40350/80000] lr: 9.375e-06, eta: 2:31:13, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2176, decode.acc_seg: 91.3534, loss: 0.2176 2023-03-03 17:56:09,422 - mmseg - INFO - Iter [40400/80000] lr: 9.375e-06, eta: 2:31:03, time: 0.252, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2163, decode.acc_seg: 91.4786, loss: 0.2163 2023-03-03 17:56:19,413 - mmseg - INFO - Iter [40450/80000] lr: 9.375e-06, eta: 2:30:50, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2107, decode.acc_seg: 91.6304, loss: 0.2107 2023-03-03 17:56:29,428 - mmseg - INFO - Iter [40500/80000] lr: 9.375e-06, eta: 2:30:37, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.5225, loss: 0.2149 2023-03-03 17:56:39,568 - mmseg - INFO - Iter [40550/80000] lr: 9.375e-06, eta: 2:30:24, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.5178, loss: 0.2153 2023-03-03 17:56:49,513 - mmseg - INFO - Iter [40600/80000] lr: 9.375e-06, eta: 2:30:12, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2134, decode.acc_seg: 91.5456, loss: 0.2134 2023-03-03 17:56:59,524 - mmseg - INFO - Iter [40650/80000] lr: 9.375e-06, eta: 2:29:59, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2100, decode.acc_seg: 91.4509, loss: 0.2100 2023-03-03 17:57:09,464 - mmseg - INFO - Iter [40700/80000] lr: 9.375e-06, eta: 2:29:46, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2029, decode.acc_seg: 91.8626, loss: 0.2029 2023-03-03 17:57:19,337 - mmseg - INFO - Iter [40750/80000] lr: 9.375e-06, eta: 2:29:33, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.5243, loss: 0.2141 2023-03-03 17:57:29,372 - mmseg - INFO - Iter [40800/80000] lr: 9.375e-06, eta: 2:29:20, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2094, decode.acc_seg: 91.7310, loss: 0.2094 2023-03-03 17:57:39,345 - mmseg - INFO - Iter [40850/80000] lr: 9.375e-06, eta: 2:29:07, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2162, decode.acc_seg: 91.3185, loss: 0.2162 2023-03-03 17:57:49,398 - mmseg - INFO - Iter [40900/80000] lr: 9.375e-06, eta: 2:28:55, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2111, decode.acc_seg: 91.6150, loss: 0.2111 2023-03-03 17:57:59,486 - mmseg - INFO - Iter [40950/80000] lr: 9.375e-06, eta: 2:28:42, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2088, decode.acc_seg: 91.6617, loss: 0.2088 2023-03-03 17:58:09,351 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 17:58:09,351 - mmseg - INFO - Iter [41000/80000] lr: 9.375e-06, eta: 2:28:29, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2085, decode.acc_seg: 91.5838, loss: 0.2085 2023-03-03 17:58:21,816 - mmseg - INFO - Iter [41050/80000] lr: 9.375e-06, eta: 2:28:19, time: 0.249, data_time: 0.052, memory: 67202, decode.loss_ce: 0.2187, decode.acc_seg: 91.1398, loss: 0.2187 2023-03-03 17:58:31,724 - mmseg - INFO - Iter [41100/80000] lr: 9.375e-06, eta: 2:28:06, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2075, decode.acc_seg: 91.8934, loss: 0.2075 2023-03-03 17:58:41,646 - mmseg - INFO - Iter [41150/80000] lr: 9.375e-06, eta: 2:27:53, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2157, decode.acc_seg: 91.3321, loss: 0.2157 2023-03-03 17:58:51,601 - mmseg - INFO - Iter [41200/80000] lr: 9.375e-06, eta: 2:27:40, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.6254, loss: 0.2099 2023-03-03 17:59:01,497 - mmseg - INFO - Iter [41250/80000] lr: 9.375e-06, eta: 2:27:27, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.3670, loss: 0.2151 2023-03-03 17:59:11,507 - mmseg - INFO - Iter [41300/80000] lr: 9.375e-06, eta: 2:27:14, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2164, decode.acc_seg: 91.4631, loss: 0.2164 2023-03-03 17:59:21,447 - mmseg - INFO - Iter [41350/80000] lr: 9.375e-06, eta: 2:27:02, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2066, decode.acc_seg: 91.5971, loss: 0.2066 2023-03-03 17:59:31,534 - mmseg - INFO - Iter [41400/80000] lr: 9.375e-06, eta: 2:26:49, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2195, decode.acc_seg: 91.3453, loss: 0.2195 2023-03-03 17:59:41,425 - mmseg - INFO - Iter [41450/80000] lr: 9.375e-06, eta: 2:26:36, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2062, decode.acc_seg: 91.6963, loss: 0.2062 2023-03-03 17:59:51,523 - mmseg - INFO - Iter [41500/80000] lr: 9.375e-06, eta: 2:26:24, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2168, decode.acc_seg: 91.3146, loss: 0.2168 2023-03-03 18:00:01,389 - mmseg - INFO - Iter [41550/80000] lr: 9.375e-06, eta: 2:26:11, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2079, decode.acc_seg: 91.7937, loss: 0.2079 2023-03-03 18:00:11,258 - mmseg - INFO - Iter [41600/80000] lr: 9.375e-06, eta: 2:25:58, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2087, decode.acc_seg: 91.8038, loss: 0.2087 2023-03-03 18:00:23,784 - mmseg - INFO - Iter [41650/80000] lr: 9.375e-06, eta: 2:25:48, time: 0.250, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2109, decode.acc_seg: 91.4669, loss: 0.2109 2023-03-03 18:00:33,749 - mmseg - INFO - Iter [41700/80000] lr: 9.375e-06, eta: 2:25:35, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.7030, loss: 0.2081 2023-03-03 18:00:43,639 - mmseg - INFO - Iter [41750/80000] lr: 9.375e-06, eta: 2:25:22, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2098, decode.acc_seg: 91.4392, loss: 0.2098 2023-03-03 18:00:53,692 - mmseg - INFO - Iter [41800/80000] lr: 9.375e-06, eta: 2:25:09, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2113, decode.acc_seg: 91.6005, loss: 0.2113 2023-03-03 18:01:03,654 - mmseg - INFO - Iter [41850/80000] lr: 9.375e-06, eta: 2:24:57, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2053, decode.acc_seg: 91.6875, loss: 0.2053 2023-03-03 18:01:13,739 - mmseg - INFO - Iter [41900/80000] lr: 9.375e-06, eta: 2:24:44, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2147, decode.acc_seg: 91.5082, loss: 0.2147 2023-03-03 18:01:23,694 - mmseg - INFO - Iter [41950/80000] lr: 9.375e-06, eta: 2:24:31, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.2928, loss: 0.2153 2023-03-03 18:01:33,760 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:01:33,760 - mmseg - INFO - Iter [42000/80000] lr: 9.375e-06, eta: 2:24:19, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2096, decode.acc_seg: 91.6894, loss: 0.2096 2023-03-03 18:01:43,687 - mmseg - INFO - Iter [42050/80000] lr: 9.375e-06, eta: 2:24:06, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2116, decode.acc_seg: 91.5914, loss: 0.2116 2023-03-03 18:01:53,764 - mmseg - INFO - Iter [42100/80000] lr: 9.375e-06, eta: 2:23:54, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.5020, loss: 0.2127 2023-03-03 18:02:03,736 - mmseg - INFO - Iter [42150/80000] lr: 9.375e-06, eta: 2:23:41, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2063, decode.acc_seg: 91.6486, loss: 0.2063 2023-03-03 18:02:13,794 - mmseg - INFO - Iter [42200/80000] lr: 9.375e-06, eta: 2:23:28, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.6414, loss: 0.2117 2023-03-03 18:02:23,841 - mmseg - INFO - Iter [42250/80000] lr: 9.375e-06, eta: 2:23:16, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2163, decode.acc_seg: 91.3177, loss: 0.2163 2023-03-03 18:02:36,241 - mmseg - INFO - Iter [42300/80000] lr: 9.375e-06, eta: 2:23:05, time: 0.248, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2190, decode.acc_seg: 91.3299, loss: 0.2190 2023-03-03 18:02:46,373 - mmseg - INFO - Iter [42350/80000] lr: 9.375e-06, eta: 2:22:53, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2158, decode.acc_seg: 91.2232, loss: 0.2158 2023-03-03 18:02:56,290 - mmseg - INFO - Iter [42400/80000] lr: 9.375e-06, eta: 2:22:40, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2204, decode.acc_seg: 91.3450, loss: 0.2204 2023-03-03 18:03:06,352 - mmseg - INFO - Iter [42450/80000] lr: 9.375e-06, eta: 2:22:27, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.5715, loss: 0.2115 2023-03-03 18:03:16,336 - mmseg - INFO - Iter [42500/80000] lr: 9.375e-06, eta: 2:22:15, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.4093, loss: 0.2140 2023-03-03 18:03:26,365 - mmseg - INFO - Iter [42550/80000] lr: 9.375e-06, eta: 2:22:02, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2071, decode.acc_seg: 91.8128, loss: 0.2071 2023-03-03 18:03:36,306 - mmseg - INFO - Iter [42600/80000] lr: 9.375e-06, eta: 2:21:50, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2082, decode.acc_seg: 91.6712, loss: 0.2082 2023-03-03 18:03:46,210 - mmseg - INFO - Iter [42650/80000] lr: 9.375e-06, eta: 2:21:37, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2147, decode.acc_seg: 91.4671, loss: 0.2147 2023-03-03 18:03:56,328 - mmseg - INFO - Iter [42700/80000] lr: 9.375e-06, eta: 2:21:24, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2152, decode.acc_seg: 91.4354, loss: 0.2152 2023-03-03 18:04:06,465 - mmseg - INFO - Iter [42750/80000] lr: 9.375e-06, eta: 2:21:12, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2130, decode.acc_seg: 91.5274, loss: 0.2130 2023-03-03 18:04:16,767 - mmseg - INFO - Iter [42800/80000] lr: 9.375e-06, eta: 2:21:00, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.4459, loss: 0.2105 2023-03-03 18:04:26,800 - mmseg - INFO - Iter [42850/80000] lr: 9.375e-06, eta: 2:20:47, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2177, decode.acc_seg: 91.2922, loss: 0.2177 2023-03-03 18:04:37,016 - mmseg - INFO - Iter [42900/80000] lr: 9.375e-06, eta: 2:20:35, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2098, decode.acc_seg: 91.5353, loss: 0.2098 2023-03-03 18:04:49,571 - mmseg - INFO - Iter [42950/80000] lr: 9.375e-06, eta: 2:20:24, time: 0.251, data_time: 0.058, memory: 67202, decode.loss_ce: 0.2062, decode.acc_seg: 91.6453, loss: 0.2062 2023-03-03 18:04:59,659 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:04:59,660 - mmseg - INFO - Iter [43000/80000] lr: 9.375e-06, eta: 2:20:12, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.4210, loss: 0.2127 2023-03-03 18:05:09,656 - mmseg - INFO - Iter [43050/80000] lr: 9.375e-06, eta: 2:19:59, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2160, decode.acc_seg: 91.2465, loss: 0.2160 2023-03-03 18:05:19,596 - mmseg - INFO - Iter [43100/80000] lr: 9.375e-06, eta: 2:19:47, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2039, decode.acc_seg: 91.7546, loss: 0.2039 2023-03-03 18:05:29,657 - mmseg - INFO - Iter [43150/80000] lr: 9.375e-06, eta: 2:19:34, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2063, decode.acc_seg: 91.5502, loss: 0.2063 2023-03-03 18:05:39,609 - mmseg - INFO - Iter [43200/80000] lr: 9.375e-06, eta: 2:19:22, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2103, decode.acc_seg: 91.4428, loss: 0.2103 2023-03-03 18:05:49,587 - mmseg - INFO - Iter [43250/80000] lr: 9.375e-06, eta: 2:19:09, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.3683, loss: 0.2211 2023-03-03 18:05:59,457 - mmseg - INFO - Iter [43300/80000] lr: 9.375e-06, eta: 2:18:57, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2103, decode.acc_seg: 91.6979, loss: 0.2103 2023-03-03 18:06:09,546 - mmseg - INFO - Iter [43350/80000] lr: 9.375e-06, eta: 2:18:44, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.5034, loss: 0.2159 2023-03-03 18:06:19,520 - mmseg - INFO - Iter [43400/80000] lr: 9.375e-06, eta: 2:18:32, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2202, decode.acc_seg: 91.3622, loss: 0.2202 2023-03-03 18:06:29,541 - mmseg - INFO - Iter [43450/80000] lr: 9.375e-06, eta: 2:18:19, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.5933, loss: 0.2086 2023-03-03 18:06:39,477 - mmseg - INFO - Iter [43500/80000] lr: 9.375e-06, eta: 2:18:07, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.4664, loss: 0.2145 2023-03-03 18:06:51,929 - mmseg - INFO - Iter [43550/80000] lr: 9.375e-06, eta: 2:17:56, time: 0.249, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2102, decode.acc_seg: 91.7837, loss: 0.2102 2023-03-03 18:07:02,107 - mmseg - INFO - Iter [43600/80000] lr: 9.375e-06, eta: 2:17:44, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2085, decode.acc_seg: 91.5921, loss: 0.2085 2023-03-03 18:07:12,161 - mmseg - INFO - Iter [43650/80000] lr: 9.375e-06, eta: 2:17:31, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.5752, loss: 0.2135 2023-03-03 18:07:22,334 - mmseg - INFO - Iter [43700/80000] lr: 9.375e-06, eta: 2:17:19, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.4858, loss: 0.2140 2023-03-03 18:07:32,332 - mmseg - INFO - Iter [43750/80000] lr: 9.375e-06, eta: 2:17:07, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.5099, loss: 0.2124 2023-03-03 18:07:42,224 - mmseg - INFO - Iter [43800/80000] lr: 9.375e-06, eta: 2:16:54, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2178, decode.acc_seg: 91.3600, loss: 0.2178 2023-03-03 18:07:52,202 - mmseg - INFO - Iter [43850/80000] lr: 9.375e-06, eta: 2:16:42, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.4794, loss: 0.2108 2023-03-03 18:08:02,381 - mmseg - INFO - Iter [43900/80000] lr: 9.375e-06, eta: 2:16:29, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2170, decode.acc_seg: 91.3954, loss: 0.2170 2023-03-03 18:08:12,310 - mmseg - INFO - Iter [43950/80000] lr: 9.375e-06, eta: 2:16:17, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2154, decode.acc_seg: 91.3499, loss: 0.2154 2023-03-03 18:08:22,290 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:08:22,290 - mmseg - INFO - Iter [44000/80000] lr: 9.375e-06, eta: 2:16:04, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2104, decode.acc_seg: 91.6100, loss: 0.2104 2023-03-03 18:08:32,339 - mmseg - INFO - Iter [44050/80000] lr: 9.375e-06, eta: 2:15:52, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.5433, loss: 0.2108 2023-03-03 18:08:42,545 - mmseg - INFO - Iter [44100/80000] lr: 9.375e-06, eta: 2:15:40, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2075, decode.acc_seg: 91.6296, loss: 0.2075 2023-03-03 18:08:52,513 - mmseg - INFO - Iter [44150/80000] lr: 9.375e-06, eta: 2:15:27, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.4892, loss: 0.2151 2023-03-03 18:09:05,229 - mmseg - INFO - Iter [44200/80000] lr: 9.375e-06, eta: 2:15:17, time: 0.254, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2178, decode.acc_seg: 91.3728, loss: 0.2178 2023-03-03 18:09:15,106 - mmseg - INFO - Iter [44250/80000] lr: 9.375e-06, eta: 2:15:05, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2109, decode.acc_seg: 91.6501, loss: 0.2109 2023-03-03 18:09:25,134 - mmseg - INFO - Iter [44300/80000] lr: 9.375e-06, eta: 2:14:52, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.3296, loss: 0.2143 2023-03-03 18:09:35,205 - mmseg - INFO - Iter [44350/80000] lr: 9.375e-06, eta: 2:14:40, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.6228, loss: 0.2115 2023-03-03 18:09:45,250 - mmseg - INFO - Iter [44400/80000] lr: 9.375e-06, eta: 2:14:27, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2168, decode.acc_seg: 91.2763, loss: 0.2168 2023-03-03 18:09:55,235 - mmseg - INFO - Iter [44450/80000] lr: 9.375e-06, eta: 2:14:15, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2103, decode.acc_seg: 91.4345, loss: 0.2103 2023-03-03 18:10:05,112 - mmseg - INFO - Iter [44500/80000] lr: 9.375e-06, eta: 2:14:03, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2157, decode.acc_seg: 91.3507, loss: 0.2157 2023-03-03 18:10:15,119 - mmseg - INFO - Iter [44550/80000] lr: 9.375e-06, eta: 2:13:50, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.8263, loss: 0.2080 2023-03-03 18:10:25,132 - mmseg - INFO - Iter [44600/80000] lr: 9.375e-06, eta: 2:13:38, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1982, decode.acc_seg: 91.8815, loss: 0.1982 2023-03-03 18:10:35,086 - mmseg - INFO - Iter [44650/80000] lr: 9.375e-06, eta: 2:13:25, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2131, decode.acc_seg: 91.4745, loss: 0.2131 2023-03-03 18:10:44,980 - mmseg - INFO - Iter [44700/80000] lr: 9.375e-06, eta: 2:13:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2102, decode.acc_seg: 91.6859, loss: 0.2102 2023-03-03 18:10:54,927 - mmseg - INFO - Iter [44750/80000] lr: 9.375e-06, eta: 2:13:01, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2165, decode.acc_seg: 91.4238, loss: 0.2165 2023-03-03 18:11:04,961 - mmseg - INFO - Iter [44800/80000] lr: 9.375e-06, eta: 2:12:48, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2137, decode.acc_seg: 91.5343, loss: 0.2137 2023-03-03 18:11:17,654 - mmseg - INFO - Iter [44850/80000] lr: 9.375e-06, eta: 2:12:38, time: 0.254, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2187, decode.acc_seg: 91.2726, loss: 0.2187 2023-03-03 18:11:27,532 - mmseg - INFO - Iter [44900/80000] lr: 9.375e-06, eta: 2:12:26, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2148, decode.acc_seg: 91.5064, loss: 0.2148 2023-03-03 18:11:37,439 - mmseg - INFO - Iter [44950/80000] lr: 9.375e-06, eta: 2:12:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.5011, loss: 0.2115 2023-03-03 18:11:47,438 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:11:47,438 - mmseg - INFO - Iter [45000/80000] lr: 9.375e-06, eta: 2:12:01, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.4685, loss: 0.2099 2023-03-03 18:11:57,355 - mmseg - INFO - Iter [45050/80000] lr: 9.375e-06, eta: 2:11:48, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.3963, loss: 0.2150 2023-03-03 18:12:07,293 - mmseg - INFO - Iter [45100/80000] lr: 9.375e-06, eta: 2:11:36, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.3289, loss: 0.2150 2023-03-03 18:12:17,185 - mmseg - INFO - Iter [45150/80000] lr: 9.375e-06, eta: 2:11:24, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2062, decode.acc_seg: 91.8029, loss: 0.2062 2023-03-03 18:12:27,219 - mmseg - INFO - Iter [45200/80000] lr: 9.375e-06, eta: 2:11:11, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2255, decode.acc_seg: 91.0321, loss: 0.2255 2023-03-03 18:12:37,207 - mmseg - INFO - Iter [45250/80000] lr: 9.375e-06, eta: 2:10:59, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2120, decode.acc_seg: 91.6321, loss: 0.2120 2023-03-03 18:12:47,149 - mmseg - INFO - Iter [45300/80000] lr: 9.375e-06, eta: 2:10:47, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2113, decode.acc_seg: 91.5819, loss: 0.2113 2023-03-03 18:12:57,089 - mmseg - INFO - Iter [45350/80000] lr: 9.375e-06, eta: 2:10:34, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.6762, loss: 0.2091 2023-03-03 18:13:07,251 - mmseg - INFO - Iter [45400/80000] lr: 9.375e-06, eta: 2:10:22, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.2556, loss: 0.2142 2023-03-03 18:13:19,733 - mmseg - INFO - Iter [45450/80000] lr: 9.375e-06, eta: 2:10:12, time: 0.250, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.4997, loss: 0.2125 2023-03-03 18:13:29,763 - mmseg - INFO - Iter [45500/80000] lr: 9.375e-06, eta: 2:09:59, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2076, decode.acc_seg: 91.6172, loss: 0.2076 2023-03-03 18:13:39,790 - mmseg - INFO - Iter [45550/80000] lr: 9.375e-06, eta: 2:09:47, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.6527, loss: 0.2099 2023-03-03 18:13:49,869 - mmseg - INFO - Iter [45600/80000] lr: 9.375e-06, eta: 2:09:35, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.6515, loss: 0.2099 2023-03-03 18:13:59,765 - mmseg - INFO - Iter [45650/80000] lr: 9.375e-06, eta: 2:09:23, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2065, decode.acc_seg: 91.7969, loss: 0.2065 2023-03-03 18:14:09,772 - mmseg - INFO - Iter [45700/80000] lr: 9.375e-06, eta: 2:09:10, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2087, decode.acc_seg: 91.5550, loss: 0.2087 2023-03-03 18:14:19,650 - mmseg - INFO - Iter [45750/80000] lr: 9.375e-06, eta: 2:08:58, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2219, decode.acc_seg: 91.2678, loss: 0.2219 2023-03-03 18:14:29,735 - mmseg - INFO - Iter [45800/80000] lr: 9.375e-06, eta: 2:08:46, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2082, decode.acc_seg: 91.5529, loss: 0.2082 2023-03-03 18:14:39,940 - mmseg - INFO - Iter [45850/80000] lr: 9.375e-06, eta: 2:08:34, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2109, decode.acc_seg: 91.5452, loss: 0.2109 2023-03-03 18:14:49,885 - mmseg - INFO - Iter [45900/80000] lr: 9.375e-06, eta: 2:08:21, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2083, decode.acc_seg: 91.6914, loss: 0.2083 2023-03-03 18:14:59,848 - mmseg - INFO - Iter [45950/80000] lr: 9.375e-06, eta: 2:08:09, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2137, decode.acc_seg: 91.4009, loss: 0.2137 2023-03-03 18:15:09,973 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:15:09,973 - mmseg - INFO - Iter [46000/80000] lr: 9.375e-06, eta: 2:07:57, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2148, decode.acc_seg: 91.5200, loss: 0.2148 2023-03-03 18:15:19,938 - mmseg - INFO - Iter [46050/80000] lr: 9.375e-06, eta: 2:07:45, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.3918, loss: 0.2135 2023-03-03 18:15:32,483 - mmseg - INFO - Iter [46100/80000] lr: 9.375e-06, eta: 2:07:34, time: 0.251, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.7574, loss: 0.2091 2023-03-03 18:15:42,428 - mmseg - INFO - Iter [46150/80000] lr: 9.375e-06, eta: 2:07:22, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2064, decode.acc_seg: 91.7702, loss: 0.2064 2023-03-03 18:15:52,284 - mmseg - INFO - Iter [46200/80000] lr: 9.375e-06, eta: 2:07:10, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2164, decode.acc_seg: 91.4137, loss: 0.2164 2023-03-03 18:16:02,282 - mmseg - INFO - Iter [46250/80000] lr: 9.375e-06, eta: 2:06:57, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2126, decode.acc_seg: 91.4386, loss: 0.2126 2023-03-03 18:16:12,249 - mmseg - INFO - Iter [46300/80000] lr: 9.375e-06, eta: 2:06:45, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2198, decode.acc_seg: 91.3065, loss: 0.2198 2023-03-03 18:16:22,523 - mmseg - INFO - Iter [46350/80000] lr: 9.375e-06, eta: 2:06:33, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2114, decode.acc_seg: 91.5043, loss: 0.2114 2023-03-03 18:16:32,517 - mmseg - INFO - Iter [46400/80000] lr: 9.375e-06, eta: 2:06:21, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2249, decode.acc_seg: 91.0676, loss: 0.2249 2023-03-03 18:16:42,572 - mmseg - INFO - Iter [46450/80000] lr: 9.375e-06, eta: 2:06:09, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2025, decode.acc_seg: 91.7682, loss: 0.2025 2023-03-03 18:16:52,548 - mmseg - INFO - Iter [46500/80000] lr: 9.375e-06, eta: 2:05:56, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.5812, loss: 0.2092 2023-03-03 18:17:02,606 - mmseg - INFO - Iter [46550/80000] lr: 9.375e-06, eta: 2:05:44, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2147, decode.acc_seg: 91.3987, loss: 0.2147 2023-03-03 18:17:12,538 - mmseg - INFO - Iter [46600/80000] lr: 9.375e-06, eta: 2:05:32, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2162, decode.acc_seg: 91.3215, loss: 0.2162 2023-03-03 18:17:22,556 - mmseg - INFO - Iter [46650/80000] lr: 9.375e-06, eta: 2:05:20, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2132, decode.acc_seg: 91.6187, loss: 0.2132 2023-03-03 18:17:35,068 - mmseg - INFO - Iter [46700/80000] lr: 9.375e-06, eta: 2:05:10, time: 0.250, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.4293, loss: 0.2142 2023-03-03 18:17:44,954 - mmseg - INFO - Iter [46750/80000] lr: 9.375e-06, eta: 2:04:57, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2100, decode.acc_seg: 91.6610, loss: 0.2100 2023-03-03 18:17:54,968 - mmseg - INFO - Iter [46800/80000] lr: 9.375e-06, eta: 2:04:45, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2053, decode.acc_seg: 91.7710, loss: 0.2053 2023-03-03 18:18:04,855 - mmseg - INFO - Iter [46850/80000] lr: 9.375e-06, eta: 2:04:33, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2138, decode.acc_seg: 91.4870, loss: 0.2138 2023-03-03 18:18:14,728 - mmseg - INFO - Iter [46900/80000] lr: 9.375e-06, eta: 2:04:21, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2134, decode.acc_seg: 91.4977, loss: 0.2134 2023-03-03 18:18:24,593 - mmseg - INFO - Iter [46950/80000] lr: 9.375e-06, eta: 2:04:08, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2146, decode.acc_seg: 91.5065, loss: 0.2146 2023-03-03 18:18:34,502 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:18:34,503 - mmseg - INFO - Iter [47000/80000] lr: 9.375e-06, eta: 2:03:56, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.6369, loss: 0.2099 2023-03-03 18:18:44,725 - mmseg - INFO - Iter [47050/80000] lr: 9.375e-06, eta: 2:03:44, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2134, decode.acc_seg: 91.5253, loss: 0.2134 2023-03-03 18:18:54,886 - mmseg - INFO - Iter [47100/80000] lr: 9.375e-06, eta: 2:03:32, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.4682, loss: 0.2142 2023-03-03 18:19:04,914 - mmseg - INFO - Iter [47150/80000] lr: 9.375e-06, eta: 2:03:20, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2102, decode.acc_seg: 91.6196, loss: 0.2102 2023-03-03 18:19:14,829 - mmseg - INFO - Iter [47200/80000] lr: 9.375e-06, eta: 2:03:08, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2120, decode.acc_seg: 91.4239, loss: 0.2120 2023-03-03 18:19:24,852 - mmseg - INFO - Iter [47250/80000] lr: 9.375e-06, eta: 2:02:56, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2067, decode.acc_seg: 91.8044, loss: 0.2067 2023-03-03 18:19:34,786 - mmseg - INFO - Iter [47300/80000] lr: 9.375e-06, eta: 2:02:43, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2152, decode.acc_seg: 91.4207, loss: 0.2152 2023-03-03 18:19:47,539 - mmseg - INFO - Iter [47350/80000] lr: 9.375e-06, eta: 2:02:33, time: 0.255, data_time: 0.052, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.5389, loss: 0.2086 2023-03-03 18:19:57,439 - mmseg - INFO - Iter [47400/80000] lr: 9.375e-06, eta: 2:02:21, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2051, decode.acc_seg: 91.6987, loss: 0.2051 2023-03-03 18:20:07,446 - mmseg - INFO - Iter [47450/80000] lr: 9.375e-06, eta: 2:02:09, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2048, decode.acc_seg: 91.7710, loss: 0.2048 2023-03-03 18:20:17,431 - mmseg - INFO - Iter [47500/80000] lr: 9.375e-06, eta: 2:01:57, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.6221, loss: 0.2081 2023-03-03 18:20:27,695 - mmseg - INFO - Iter [47550/80000] lr: 9.375e-06, eta: 2:01:45, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2287, decode.acc_seg: 90.9288, loss: 0.2287 2023-03-03 18:20:37,626 - mmseg - INFO - Iter [47600/80000] lr: 9.375e-06, eta: 2:01:33, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.4683, loss: 0.2099 2023-03-03 18:20:47,563 - mmseg - INFO - Iter [47650/80000] lr: 9.375e-06, eta: 2:01:21, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.4660, loss: 0.2149 2023-03-03 18:20:57,451 - mmseg - INFO - Iter [47700/80000] lr: 9.375e-06, eta: 2:01:08, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2167, decode.acc_seg: 91.3046, loss: 0.2167 2023-03-03 18:21:07,672 - mmseg - INFO - Iter [47750/80000] lr: 9.375e-06, eta: 2:00:56, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.6066, loss: 0.2090 2023-03-03 18:21:17,840 - mmseg - INFO - Iter [47800/80000] lr: 9.375e-06, eta: 2:00:44, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.5373, loss: 0.2110 2023-03-03 18:21:28,026 - mmseg - INFO - Iter [47850/80000] lr: 9.375e-06, eta: 2:00:32, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2167, decode.acc_seg: 91.3688, loss: 0.2167 2023-03-03 18:21:38,209 - mmseg - INFO - Iter [47900/80000] lr: 9.375e-06, eta: 2:00:20, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.4629, loss: 0.2149 2023-03-03 18:21:48,146 - mmseg - INFO - Iter [47950/80000] lr: 9.375e-06, eta: 2:00:08, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.4778, loss: 0.2181 2023-03-03 18:22:00,734 - mmseg - INFO - Saving checkpoint at 48000 iterations 2023-03-03 18:22:01,617 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:22:01,617 - mmseg - INFO - Iter [48000/80000] lr: 9.375e-06, eta: 1:59:59, time: 0.269, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2232, decode.acc_seg: 91.2111, loss: 0.2232 2023-03-03 18:22:16,359 - mmseg - INFO - per class results: 2023-03-03 18:22:16,364 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 75.92 | 88.23 | | building | 82.34 | 93.54 | | sky | 94.17 | 97.08 | | floor | 79.06 | 90.35 | | tree | 73.04 | 88.23 | | ceiling | 82.17 | 91.27 | | road | 81.36 | 90.0 | | bed | 87.41 | 95.5 | | windowpane | 59.39 | 76.05 | | grass | 65.71 | 83.69 | | cabinet | 57.37 | 70.89 | | sidewalk | 65.56 | 78.47 | | person | 77.69 | 91.25 | | earth | 32.16 | 45.43 | | door | 45.39 | 59.24 | | table | 59.39 | 76.03 | | mountain | 51.34 | 68.58 | | plant | 50.92 | 63.05 | | curtain | 71.09 | 83.25 | | chair | 54.96 | 69.7 | | car | 81.11 | 90.17 | | water | 45.29 | 60.48 | | painting | 70.96 | 85.37 | | sofa | 63.73 | 81.94 | | shelf | 38.15 | 54.13 | | house | 45.95 | 54.69 | | sea | 43.08 | 70.63 | | mirror | 63.6 | 71.21 | | rug | 56.55 | 63.15 | | field | 23.62 | 36.23 | | armchair | 41.08 | 56.32 | | seat | 57.5 | 78.11 | | fence | 34.46 | 46.94 | | desk | 48.54 | 67.32 | | rock | 28.3 | 44.88 | | wardrobe | 44.69 | 62.56 | | lamp | 62.34 | 75.08 | | bathtub | 75.93 | 82.47 | | railing | 27.37 | 40.14 | | cushion | 53.26 | 65.22 | | base | 20.68 | 32.7 | | box | 20.29 | 24.45 | | column | 44.91 | 57.31 | | signboard | 36.0 | 49.41 | | chest of drawers | 37.81 | 59.32 | | counter | 27.71 | 34.8 | | sand | 32.03 | 47.07 | | sink | 67.62 | 78.31 | | skyscraper | 66.89 | 74.98 | | fireplace | 70.82 | 86.99 | | refrigerator | 69.29 | 79.07 | | grandstand | 39.92 | 64.31 | | path | 15.3 | 21.16 | | stairs | 30.41 | 35.65 | | runway | 58.54 | 77.19 | | case | 43.69 | 66.22 | | pool table | 91.37 | 96.06 | | pillow | 54.18 | 65.89 | | screen door | 65.56 | 70.22 | | stairway | 31.21 | 39.79 | | river | 12.29 | 22.67 | | bridge | 63.63 | 70.19 | | bookcase | 39.37 | 48.36 | | blind | 40.47 | 46.43 | | coffee table | 57.85 | 76.37 | | toilet | 85.45 | 90.79 | | flower | 34.55 | 45.87 | | book | 45.37 | 63.38 | | hill | 5.02 | 6.87 | | bench | 37.26 | 47.43 | | countertop | 55.59 | 72.23 | | stove | 72.58 | 81.1 | | palm | 51.58 | 73.17 | | kitchen island | 45.69 | 77.97 | | computer | 54.65 | 64.38 | | swivel chair | 44.58 | 59.38 | | boat | 47.18 | 59.88 | | bar | 25.06 | 30.61 | | arcade machine | 26.17 | 30.41 | | hovel | 39.43 | 43.52 | | bus | 79.38 | 86.82 | | towel | 56.98 | 65.72 | | light | 53.13 | 60.09 | | truck | 31.93 | 42.74 | | tower | 32.99 | 40.03 | | chandelier | 67.69 | 83.84 | | awning | 25.19 | 28.16 | | streetlight | 25.32 | 30.99 | | booth | 40.72 | 45.32 | | television receiver | 67.13 | 78.17 | | airplane | 50.06 | 61.81 | | dirt track | 2.73 | 5.72 | | apparel | 29.04 | 42.58 | | pole | 23.31 | 35.07 | | land | 0.71 | 1.04 | | bannister | 9.81 | 12.57 | | escalator | 20.19 | 20.84 | | ottoman | 42.26 | 55.21 | | bottle | 12.89 | 20.73 | | buffet | 34.18 | 43.33 | | poster | 25.51 | 34.71 | | stage | 10.85 | 14.5 | | van | 41.56 | 57.45 | | ship | 66.74 | 76.04 | | fountain | 0.44 | 0.44 | | conveyer belt | 59.36 | 86.12 | | canopy | 14.98 | 17.66 | | washer | 63.11 | 65.35 | | plaything | 20.89 | 25.45 | | swimming pool | 29.03 | 35.24 | | stool | 40.48 | 51.35 | | barrel | 34.71 | 64.83 | | basket | 22.82 | 33.13 | | waterfall | 57.99 | 78.86 | | tent | 91.67 | 98.47 | | bag | 8.54 | 10.06 | | minibike | 48.62 | 55.13 | | cradle | 75.81 | 96.99 | | oven | 23.46 | 55.66 | | ball | 46.23 | 66.58 | | food | 49.79 | 57.23 | | step | 3.58 | 4.43 | | tank | 44.91 | 45.43 | | trade name | 22.24 | 25.74 | | microwave | 38.08 | 40.77 | | pot | 35.97 | 41.96 | | animal | 50.8 | 52.77 | | bicycle | 45.52 | 70.22 | | lake | 59.25 | 63.09 | | dishwasher | 71.95 | 77.03 | | screen | 59.64 | 73.58 | | blanket | 6.77 | 8.11 | | sculpture | 40.15 | 63.05 | | hood | 60.29 | 69.09 | | sconce | 40.44 | 46.32 | | vase | 32.11 | 48.28 | | traffic light | 27.59 | 40.97 | | tray | 4.5 | 6.5 | | ashcan | 40.58 | 52.88 | | fan | 56.36 | 69.46 | | pier | 20.81 | 26.15 | | crt screen | 4.3 | 7.61 | | plate | 38.79 | 49.29 | | monitor | 62.44 | 75.13 | | bulletin board | 34.16 | 47.33 | | shower | 1.34 | 1.86 | | radiator | 40.92 | 48.53 | | glass | 9.92 | 10.97 | | clock | 19.83 | 24.73 | | flag | 40.48 | 43.26 | +---------------------+-------+-------+ 2023-03-03 18:22:16,365 - mmseg - INFO - Summary: 2023-03-03 18:22:16,365 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.58 | 44.75 | 55.57 | +-------+-------+-------+ 2023-03-03 18:22:16,394 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_32000.pth was removed 2023-03-03 18:22:17,208 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_48000.pth. 2023-03-03 18:22:17,209 - mmseg - INFO - Best mIoU is 0.4475 at 48000 iter. 2023-03-03 18:22:17,209 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:22:17,209 - mmseg - INFO - Iter(val) [250] aAcc: 0.8158, mIoU: 0.4475, mAcc: 0.5557, IoU.background: nan, IoU.wall: 0.7592, IoU.building: 0.8234, IoU.sky: 0.9417, IoU.floor: 0.7906, IoU.tree: 0.7304, IoU.ceiling: 0.8217, IoU.road: 0.8136, IoU.bed : 0.8741, IoU.windowpane: 0.5939, IoU.grass: 0.6571, IoU.cabinet: 0.5737, IoU.sidewalk: 0.6556, IoU.person: 0.7769, IoU.earth: 0.3216, IoU.door: 0.4539, IoU.table: 0.5939, IoU.mountain: 0.5134, IoU.plant: 0.5092, IoU.curtain: 0.7109, IoU.chair: 0.5496, IoU.car: 0.8111, IoU.water: 0.4529, IoU.painting: 0.7096, IoU.sofa: 0.6373, IoU.shelf: 0.3815, IoU.house: 0.4595, IoU.sea: 0.4308, IoU.mirror: 0.6360, IoU.rug: 0.5655, IoU.field: 0.2362, IoU.armchair: 0.4108, IoU.seat: 0.5750, IoU.fence: 0.3446, IoU.desk: 0.4854, IoU.rock: 0.2830, IoU.wardrobe: 0.4469, IoU.lamp: 0.6234, IoU.bathtub: 0.7593, IoU.railing: 0.2737, IoU.cushion: 0.5326, IoU.base: 0.2068, IoU.box: 0.2029, IoU.column: 0.4491, IoU.signboard: 0.3600, IoU.chest of drawers: 0.3781, IoU.counter: 0.2771, IoU.sand: 0.3203, IoU.sink: 0.6762, IoU.skyscraper: 0.6689, IoU.fireplace: 0.7082, IoU.refrigerator: 0.6929, IoU.grandstand: 0.3992, IoU.path: 0.1530, IoU.stairs: 0.3041, IoU.runway: 0.5854, IoU.case: 0.4369, IoU.pool table: 0.9137, IoU.pillow: 0.5418, IoU.screen door: 0.6556, IoU.stairway: 0.3121, IoU.river: 0.1229, IoU.bridge: 0.6363, IoU.bookcase: 0.3937, IoU.blind: 0.4047, IoU.coffee table: 0.5785, IoU.toilet: 0.8545, IoU.flower: 0.3455, IoU.book: 0.4537, IoU.hill: 0.0502, IoU.bench: 0.3726, IoU.countertop: 0.5559, IoU.stove: 0.7258, IoU.palm: 0.5158, IoU.kitchen island: 0.4569, IoU.computer: 0.5465, IoU.swivel chair: 0.4458, IoU.boat: 0.4718, IoU.bar: 0.2506, IoU.arcade machine: 0.2617, IoU.hovel: 0.3943, IoU.bus: 0.7938, IoU.towel: 0.5698, IoU.light: 0.5313, IoU.truck: 0.3193, IoU.tower: 0.3299, IoU.chandelier: 0.6769, IoU.awning: 0.2519, IoU.streetlight: 0.2532, IoU.booth: 0.4072, IoU.television receiver: 0.6713, IoU.airplane: 0.5006, IoU.dirt track: 0.0273, IoU.apparel: 0.2904, IoU.pole: 0.2331, IoU.land: 0.0071, IoU.bannister: 0.0981, IoU.escalator: 0.2019, IoU.ottoman: 0.4226, IoU.bottle: 0.1289, IoU.buffet: 0.3418, IoU.poster: 0.2551, IoU.stage: 0.1085, IoU.van: 0.4156, IoU.ship: 0.6674, IoU.fountain: 0.0044, IoU.conveyer belt: 0.5936, IoU.canopy: 0.1498, IoU.washer: 0.6311, IoU.plaything: 0.2089, IoU.swimming pool: 0.2903, IoU.stool: 0.4048, IoU.barrel: 0.3471, IoU.basket: 0.2282, IoU.waterfall: 0.5799, IoU.tent: 0.9167, IoU.bag: 0.0854, IoU.minibike: 0.4862, IoU.cradle: 0.7581, IoU.oven: 0.2346, IoU.ball: 0.4623, IoU.food: 0.4979, IoU.step: 0.0358, IoU.tank: 0.4491, IoU.trade name: 0.2224, IoU.microwave: 0.3808, IoU.pot: 0.3597, IoU.animal: 0.5080, IoU.bicycle: 0.4552, IoU.lake: 0.5925, IoU.dishwasher: 0.7195, IoU.screen: 0.5964, IoU.blanket: 0.0677, IoU.sculpture: 0.4015, IoU.hood: 0.6029, IoU.sconce: 0.4044, IoU.vase: 0.3211, IoU.traffic light: 0.2759, IoU.tray: 0.0450, IoU.ashcan: 0.4058, IoU.fan: 0.5636, IoU.pier: 0.2081, IoU.crt screen: 0.0430, IoU.plate: 0.3879, IoU.monitor: 0.6244, IoU.bulletin board: 0.3416, IoU.shower: 0.0134, IoU.radiator: 0.4092, IoU.glass: 0.0992, IoU.clock: 0.1983, IoU.flag: 0.4048, Acc.background: nan, Acc.wall: 0.8823, Acc.building: 0.9354, Acc.sky: 0.9708, Acc.floor: 0.9035, Acc.tree: 0.8823, Acc.ceiling: 0.9127, Acc.road: 0.9000, Acc.bed : 0.9550, Acc.windowpane: 0.7605, Acc.grass: 0.8369, Acc.cabinet: 0.7089, Acc.sidewalk: 0.7847, Acc.person: 0.9125, Acc.earth: 0.4543, Acc.door: 0.5924, Acc.table: 0.7603, Acc.mountain: 0.6858, Acc.plant: 0.6305, Acc.curtain: 0.8325, Acc.chair: 0.6970, Acc.car: 0.9017, Acc.water: 0.6048, Acc.painting: 0.8537, Acc.sofa: 0.8194, Acc.shelf: 0.5413, Acc.house: 0.5469, Acc.sea: 0.7063, Acc.mirror: 0.7121, Acc.rug: 0.6315, Acc.field: 0.3623, Acc.armchair: 0.5632, Acc.seat: 0.7811, Acc.fence: 0.4694, Acc.desk: 0.6732, Acc.rock: 0.4488, Acc.wardrobe: 0.6256, Acc.lamp: 0.7508, Acc.bathtub: 0.8247, Acc.railing: 0.4014, Acc.cushion: 0.6522, Acc.base: 0.3270, Acc.box: 0.2445, Acc.column: 0.5731, Acc.signboard: 0.4941, Acc.chest of drawers: 0.5932, Acc.counter: 0.3480, Acc.sand: 0.4707, Acc.sink: 0.7831, Acc.skyscraper: 0.7498, Acc.fireplace: 0.8699, Acc.refrigerator: 0.7907, Acc.grandstand: 0.6431, Acc.path: 0.2116, Acc.stairs: 0.3565, Acc.runway: 0.7719, Acc.case: 0.6622, Acc.pool table: 0.9606, Acc.pillow: 0.6589, Acc.screen door: 0.7022, Acc.stairway: 0.3979, Acc.river: 0.2267, Acc.bridge: 0.7019, Acc.bookcase: 0.4836, Acc.blind: 0.4643, Acc.coffee table: 0.7637, Acc.toilet: 0.9079, Acc.flower: 0.4587, Acc.book: 0.6338, Acc.hill: 0.0687, Acc.bench: 0.4743, Acc.countertop: 0.7223, Acc.stove: 0.8110, Acc.palm: 0.7317, Acc.kitchen island: 0.7797, Acc.computer: 0.6438, Acc.swivel chair: 0.5938, Acc.boat: 0.5988, Acc.bar: 0.3061, Acc.arcade machine: 0.3041, Acc.hovel: 0.4352, Acc.bus: 0.8682, Acc.towel: 0.6572, Acc.light: 0.6009, Acc.truck: 0.4274, Acc.tower: 0.4003, Acc.chandelier: 0.8384, Acc.awning: 0.2816, Acc.streetlight: 0.3099, Acc.booth: 0.4532, Acc.television receiver: 0.7817, Acc.airplane: 0.6181, Acc.dirt track: 0.0572, Acc.apparel: 0.4258, Acc.pole: 0.3507, Acc.land: 0.0104, Acc.bannister: 0.1257, Acc.escalator: 0.2084, Acc.ottoman: 0.5521, Acc.bottle: 0.2073, Acc.buffet: 0.4333, Acc.poster: 0.3471, Acc.stage: 0.1450, Acc.van: 0.5745, Acc.ship: 0.7604, Acc.fountain: 0.0044, Acc.conveyer belt: 0.8612, Acc.canopy: 0.1766, Acc.washer: 0.6535, Acc.plaything: 0.2545, Acc.swimming pool: 0.3524, Acc.stool: 0.5135, Acc.barrel: 0.6483, Acc.basket: 0.3313, Acc.waterfall: 0.7886, Acc.tent: 0.9847, Acc.bag: 0.1006, Acc.minibike: 0.5513, Acc.cradle: 0.9699, Acc.oven: 0.5566, Acc.ball: 0.6658, Acc.food: 0.5723, Acc.step: 0.0443, Acc.tank: 0.4543, Acc.trade name: 0.2574, Acc.microwave: 0.4077, Acc.pot: 0.4196, Acc.animal: 0.5277, Acc.bicycle: 0.7022, Acc.lake: 0.6309, Acc.dishwasher: 0.7703, Acc.screen: 0.7358, Acc.blanket: 0.0811, Acc.sculpture: 0.6305, Acc.hood: 0.6909, Acc.sconce: 0.4632, Acc.vase: 0.4828, Acc.traffic light: 0.4097, Acc.tray: 0.0650, Acc.ashcan: 0.5288, Acc.fan: 0.6946, Acc.pier: 0.2615, Acc.crt screen: 0.0761, Acc.plate: 0.4929, Acc.monitor: 0.7513, Acc.bulletin board: 0.4733, Acc.shower: 0.0186, Acc.radiator: 0.4853, Acc.glass: 0.1097, Acc.clock: 0.2473, Acc.flag: 0.4326 2023-03-03 18:22:27,711 - mmseg - INFO - Iter [48050/80000] lr: 9.375e-06, eta: 1:59:57, time: 0.522, data_time: 0.319, memory: 67202, decode.loss_ce: 0.2079, decode.acc_seg: 91.7319, loss: 0.2079 2023-03-03 18:22:37,878 - mmseg - INFO - Iter [48100/80000] lr: 9.375e-06, eta: 1:59:45, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.4825, loss: 0.2153 2023-03-03 18:22:47,925 - mmseg - INFO - Iter [48150/80000] lr: 9.375e-06, eta: 1:59:33, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.4995, loss: 0.2125 2023-03-03 18:22:57,943 - mmseg - INFO - Iter [48200/80000] lr: 9.375e-06, eta: 1:59:21, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2096, decode.acc_seg: 91.5957, loss: 0.2096 2023-03-03 18:23:07,876 - mmseg - INFO - Iter [48250/80000] lr: 9.375e-06, eta: 1:59:09, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2132, decode.acc_seg: 91.4250, loss: 0.2132 2023-03-03 18:23:17,828 - mmseg - INFO - Iter [48300/80000] lr: 9.375e-06, eta: 1:58:57, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.4614, loss: 0.2110 2023-03-03 18:23:27,794 - mmseg - INFO - Iter [48350/80000] lr: 9.375e-06, eta: 1:58:45, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2132, decode.acc_seg: 91.4713, loss: 0.2132 2023-03-03 18:23:37,745 - mmseg - INFO - Iter [48400/80000] lr: 9.375e-06, eta: 1:58:33, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.4605, loss: 0.2140 2023-03-03 18:23:47,797 - mmseg - INFO - Iter [48450/80000] lr: 9.375e-06, eta: 1:58:21, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2094, decode.acc_seg: 91.7723, loss: 0.2094 2023-03-03 18:23:57,801 - mmseg - INFO - Iter [48500/80000] lr: 9.375e-06, eta: 1:58:08, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.5827, loss: 0.2128 2023-03-03 18:24:07,920 - mmseg - INFO - Iter [48550/80000] lr: 9.375e-06, eta: 1:57:57, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2046, decode.acc_seg: 91.7199, loss: 0.2046 2023-03-03 18:24:20,320 - mmseg - INFO - Iter [48600/80000] lr: 9.375e-06, eta: 1:57:46, time: 0.248, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.5246, loss: 0.2108 2023-03-03 18:24:30,327 - mmseg - INFO - Iter [48650/80000] lr: 9.375e-06, eta: 1:57:34, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2152, decode.acc_seg: 91.3412, loss: 0.2152 2023-03-03 18:24:40,317 - mmseg - INFO - Iter [48700/80000] lr: 9.375e-06, eta: 1:57:22, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2119, decode.acc_seg: 91.5694, loss: 0.2119 2023-03-03 18:24:50,263 - mmseg - INFO - Iter [48750/80000] lr: 9.375e-06, eta: 1:57:10, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2062, decode.acc_seg: 91.7122, loss: 0.2062 2023-03-03 18:25:00,212 - mmseg - INFO - Iter [48800/80000] lr: 9.375e-06, eta: 1:56:58, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2167, decode.acc_seg: 91.4046, loss: 0.2167 2023-03-03 18:25:10,202 - mmseg - INFO - Iter [48850/80000] lr: 9.375e-06, eta: 1:56:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2089, decode.acc_seg: 91.6716, loss: 0.2089 2023-03-03 18:25:20,199 - mmseg - INFO - Iter [48900/80000] lr: 9.375e-06, eta: 1:56:34, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.2684, loss: 0.2151 2023-03-03 18:25:30,698 - mmseg - INFO - Iter [48950/80000] lr: 9.375e-06, eta: 1:56:22, time: 0.210, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.4462, loss: 0.2115 2023-03-03 18:25:40,661 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:25:40,661 - mmseg - INFO - Iter [49000/80000] lr: 9.375e-06, eta: 1:56:10, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.7235, loss: 0.2086 2023-03-03 18:25:50,696 - mmseg - INFO - Iter [49050/80000] lr: 9.375e-06, eta: 1:55:58, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2023, decode.acc_seg: 91.8321, loss: 0.2023 2023-03-03 18:26:00,810 - mmseg - INFO - Iter [49100/80000] lr: 9.375e-06, eta: 1:55:46, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2190, decode.acc_seg: 91.3018, loss: 0.2190 2023-03-03 18:26:10,803 - mmseg - INFO - Iter [49150/80000] lr: 9.375e-06, eta: 1:55:34, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2121, decode.acc_seg: 91.4621, loss: 0.2121 2023-03-03 18:26:20,747 - mmseg - INFO - Iter [49200/80000] lr: 9.375e-06, eta: 1:55:22, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.6135, loss: 0.2124 2023-03-03 18:26:33,311 - mmseg - INFO - Iter [49250/80000] lr: 9.375e-06, eta: 1:55:11, time: 0.251, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2089, decode.acc_seg: 91.6661, loss: 0.2089 2023-03-03 18:26:43,216 - mmseg - INFO - Iter [49300/80000] lr: 9.375e-06, eta: 1:54:59, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.5844, loss: 0.2142 2023-03-03 18:26:53,291 - mmseg - INFO - Iter [49350/80000] lr: 9.375e-06, eta: 1:54:47, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2068, decode.acc_seg: 91.5532, loss: 0.2068 2023-03-03 18:27:03,278 - mmseg - INFO - Iter [49400/80000] lr: 9.375e-06, eta: 1:54:35, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2079, decode.acc_seg: 91.6424, loss: 0.2079 2023-03-03 18:27:13,198 - mmseg - INFO - Iter [49450/80000] lr: 9.375e-06, eta: 1:54:23, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2111, decode.acc_seg: 91.6493, loss: 0.2111 2023-03-03 18:27:23,042 - mmseg - INFO - Iter [49500/80000] lr: 9.375e-06, eta: 1:54:11, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2100, decode.acc_seg: 91.4141, loss: 0.2100 2023-03-03 18:27:32,961 - mmseg - INFO - Iter [49550/80000] lr: 9.375e-06, eta: 1:53:59, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.4081, loss: 0.2153 2023-03-03 18:27:42,887 - mmseg - INFO - Iter [49600/80000] lr: 9.375e-06, eta: 1:53:47, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2077, decode.acc_seg: 91.6743, loss: 0.2077 2023-03-03 18:27:52,886 - mmseg - INFO - Iter [49650/80000] lr: 9.375e-06, eta: 1:53:35, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.6635, loss: 0.2086 2023-03-03 18:28:02,733 - mmseg - INFO - Iter [49700/80000] lr: 9.375e-06, eta: 1:53:23, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2089, decode.acc_seg: 91.5158, loss: 0.2089 2023-03-03 18:28:12,828 - mmseg - INFO - Iter [49750/80000] lr: 9.375e-06, eta: 1:53:11, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2155, decode.acc_seg: 91.4199, loss: 0.2155 2023-03-03 18:28:22,721 - mmseg - INFO - Iter [49800/80000] lr: 9.375e-06, eta: 1:52:59, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2123, decode.acc_seg: 91.5075, loss: 0.2123 2023-03-03 18:28:35,546 - mmseg - INFO - Iter [49850/80000] lr: 9.375e-06, eta: 1:52:49, time: 0.256, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.5884, loss: 0.2117 2023-03-03 18:28:45,551 - mmseg - INFO - Iter [49900/80000] lr: 9.375e-06, eta: 1:52:37, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2121, decode.acc_seg: 91.4762, loss: 0.2121 2023-03-03 18:28:55,633 - mmseg - INFO - Iter [49950/80000] lr: 9.375e-06, eta: 1:52:25, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.3141, loss: 0.2141 2023-03-03 18:29:05,525 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:29:05,525 - mmseg - INFO - Iter [50000/80000] lr: 9.375e-06, eta: 1:52:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.5061, loss: 0.2117 2023-03-03 18:29:15,552 - mmseg - INFO - Iter [50050/80000] lr: 4.687e-06, eta: 1:52:01, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.3797, loss: 0.2149 2023-03-03 18:29:25,567 - mmseg - INFO - Iter [50100/80000] lr: 4.687e-06, eta: 1:51:49, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.5673, loss: 0.2081 2023-03-03 18:29:35,616 - mmseg - INFO - Iter [50150/80000] lr: 4.687e-06, eta: 1:51:37, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2047, decode.acc_seg: 91.7197, loss: 0.2047 2023-03-03 18:29:45,642 - mmseg - INFO - Iter [50200/80000] lr: 4.687e-06, eta: 1:51:25, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.5499, loss: 0.2135 2023-03-03 18:29:55,779 - mmseg - INFO - Iter [50250/80000] lr: 4.687e-06, eta: 1:51:13, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2130, decode.acc_seg: 91.4684, loss: 0.2130 2023-03-03 18:30:05,735 - mmseg - INFO - Iter [50300/80000] lr: 4.687e-06, eta: 1:51:01, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2051, decode.acc_seg: 91.7449, loss: 0.2051 2023-03-03 18:30:15,764 - mmseg - INFO - Iter [50350/80000] lr: 4.687e-06, eta: 1:50:50, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.5836, loss: 0.2122 2023-03-03 18:30:25,893 - mmseg - INFO - Iter [50400/80000] lr: 4.687e-06, eta: 1:50:38, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2071, decode.acc_seg: 91.6243, loss: 0.2071 2023-03-03 18:30:35,897 - mmseg - INFO - Iter [50450/80000] lr: 4.687e-06, eta: 1:50:26, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1971, decode.acc_seg: 92.2005, loss: 0.1971 2023-03-03 18:30:48,347 - mmseg - INFO - Iter [50500/80000] lr: 4.687e-06, eta: 1:50:15, time: 0.249, data_time: 0.052, memory: 67202, decode.loss_ce: 0.2073, decode.acc_seg: 91.6446, loss: 0.2073 2023-03-03 18:30:58,325 - mmseg - INFO - Iter [50550/80000] lr: 4.687e-06, eta: 1:50:03, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.6283, loss: 0.2108 2023-03-03 18:31:08,208 - mmseg - INFO - Iter [50600/80000] lr: 4.687e-06, eta: 1:49:51, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2078, decode.acc_seg: 91.6677, loss: 0.2078 2023-03-03 18:31:18,169 - mmseg - INFO - Iter [50650/80000] lr: 4.687e-06, eta: 1:49:39, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2057, decode.acc_seg: 91.6651, loss: 0.2057 2023-03-03 18:31:28,094 - mmseg - INFO - Iter [50700/80000] lr: 4.687e-06, eta: 1:49:27, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2093, decode.acc_seg: 91.7452, loss: 0.2093 2023-03-03 18:31:38,049 - mmseg - INFO - Iter [50750/80000] lr: 4.687e-06, eta: 1:49:16, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2059, decode.acc_seg: 91.7438, loss: 0.2059 2023-03-03 18:31:47,957 - mmseg - INFO - Iter [50800/80000] lr: 4.687e-06, eta: 1:49:04, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2154, decode.acc_seg: 91.3883, loss: 0.2154 2023-03-03 18:31:57,820 - mmseg - INFO - Iter [50850/80000] lr: 4.687e-06, eta: 1:48:52, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2075, decode.acc_seg: 91.7636, loss: 0.2075 2023-03-03 18:32:07,689 - mmseg - INFO - Iter [50900/80000] lr: 4.687e-06, eta: 1:48:40, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2073, decode.acc_seg: 91.6634, loss: 0.2073 2023-03-03 18:32:17,878 - mmseg - INFO - Iter [50950/80000] lr: 4.687e-06, eta: 1:48:28, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2095, decode.acc_seg: 91.6628, loss: 0.2095 2023-03-03 18:32:27,735 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:32:27,735 - mmseg - INFO - Iter [51000/80000] lr: 4.687e-06, eta: 1:48:16, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.3926, loss: 0.2140 2023-03-03 18:32:37,663 - mmseg - INFO - Iter [51050/80000] lr: 4.687e-06, eta: 1:48:04, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.7327, loss: 0.2080 2023-03-03 18:32:47,653 - mmseg - INFO - Iter [51100/80000] lr: 4.687e-06, eta: 1:47:52, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2154, decode.acc_seg: 91.1314, loss: 0.2154 2023-03-03 18:33:00,286 - mmseg - INFO - Iter [51150/80000] lr: 4.687e-06, eta: 1:47:42, time: 0.253, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2093, decode.acc_seg: 91.5652, loss: 0.2093 2023-03-03 18:33:10,365 - mmseg - INFO - Iter [51200/80000] lr: 4.687e-06, eta: 1:47:30, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2096, decode.acc_seg: 91.5852, loss: 0.2096 2023-03-03 18:33:20,503 - mmseg - INFO - Iter [51250/80000] lr: 4.687e-06, eta: 1:47:18, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2078, decode.acc_seg: 91.7357, loss: 0.2078 2023-03-03 18:33:30,375 - mmseg - INFO - Iter [51300/80000] lr: 4.687e-06, eta: 1:47:06, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.6298, loss: 0.2110 2023-03-03 18:33:40,510 - mmseg - INFO - Iter [51350/80000] lr: 4.687e-06, eta: 1:46:54, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.6609, loss: 0.2127 2023-03-03 18:33:50,461 - mmseg - INFO - Iter [51400/80000] lr: 4.687e-06, eta: 1:46:42, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2220, decode.acc_seg: 91.1210, loss: 0.2220 2023-03-03 18:34:00,791 - mmseg - INFO - Iter [51450/80000] lr: 4.687e-06, eta: 1:46:31, time: 0.207, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2044, decode.acc_seg: 91.7704, loss: 0.2044 2023-03-03 18:34:10,676 - mmseg - INFO - Iter [51500/80000] lr: 4.687e-06, eta: 1:46:19, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.4997, loss: 0.2092 2023-03-03 18:34:20,641 - mmseg - INFO - Iter [51550/80000] lr: 4.687e-06, eta: 1:46:07, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2203, decode.acc_seg: 91.2660, loss: 0.2203 2023-03-03 18:34:30,646 - mmseg - INFO - Iter [51600/80000] lr: 4.687e-06, eta: 1:45:55, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.3881, loss: 0.2110 2023-03-03 18:34:40,757 - mmseg - INFO - Iter [51650/80000] lr: 4.687e-06, eta: 1:45:43, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2121, decode.acc_seg: 91.5348, loss: 0.2121 2023-03-03 18:34:50,751 - mmseg - INFO - Iter [51700/80000] lr: 4.687e-06, eta: 1:45:32, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.3913, loss: 0.2127 2023-03-03 18:35:03,471 - mmseg - INFO - Iter [51750/80000] lr: 4.687e-06, eta: 1:45:21, time: 0.254, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.2651, loss: 0.2212 2023-03-03 18:35:13,439 - mmseg - INFO - Iter [51800/80000] lr: 4.687e-06, eta: 1:45:09, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.6383, loss: 0.2099 2023-03-03 18:35:23,481 - mmseg - INFO - Iter [51850/80000] lr: 4.687e-06, eta: 1:44:58, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2131, decode.acc_seg: 91.2899, loss: 0.2131 2023-03-03 18:35:33,608 - mmseg - INFO - Iter [51900/80000] lr: 4.687e-06, eta: 1:44:46, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2188, decode.acc_seg: 91.0965, loss: 0.2188 2023-03-03 18:35:43,485 - mmseg - INFO - Iter [51950/80000] lr: 4.687e-06, eta: 1:44:34, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2095, decode.acc_seg: 91.6394, loss: 0.2095 2023-03-03 18:35:53,379 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:35:53,379 - mmseg - INFO - Iter [52000/80000] lr: 4.687e-06, eta: 1:44:22, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2063, decode.acc_seg: 91.7397, loss: 0.2063 2023-03-03 18:36:03,389 - mmseg - INFO - Iter [52050/80000] lr: 4.687e-06, eta: 1:44:10, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2058, decode.acc_seg: 91.7951, loss: 0.2058 2023-03-03 18:36:13,342 - mmseg - INFO - Iter [52100/80000] lr: 4.687e-06, eta: 1:43:58, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2083, decode.acc_seg: 91.5840, loss: 0.2083 2023-03-03 18:36:23,254 - mmseg - INFO - Iter [52150/80000] lr: 4.687e-06, eta: 1:43:46, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2147, decode.acc_seg: 91.4779, loss: 0.2147 2023-03-03 18:36:33,342 - mmseg - INFO - Iter [52200/80000] lr: 4.687e-06, eta: 1:43:35, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2104, decode.acc_seg: 91.7302, loss: 0.2104 2023-03-03 18:36:43,452 - mmseg - INFO - Iter [52250/80000] lr: 4.687e-06, eta: 1:43:23, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2017, decode.acc_seg: 91.8681, loss: 0.2017 2023-03-03 18:36:53,426 - mmseg - INFO - Iter [52300/80000] lr: 4.687e-06, eta: 1:43:11, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.5745, loss: 0.2108 2023-03-03 18:37:03,467 - mmseg - INFO - Iter [52350/80000] lr: 4.687e-06, eta: 1:42:59, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.6211, loss: 0.2090 2023-03-03 18:37:15,881 - mmseg - INFO - Iter [52400/80000] lr: 4.687e-06, eta: 1:42:49, time: 0.248, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2114, decode.acc_seg: 91.5599, loss: 0.2114 2023-03-03 18:37:25,785 - mmseg - INFO - Iter [52450/80000] lr: 4.687e-06, eta: 1:42:37, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.6685, loss: 0.2099 2023-03-03 18:37:35,676 - mmseg - INFO - Iter [52500/80000] lr: 4.687e-06, eta: 1:42:25, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2114, decode.acc_seg: 91.6145, loss: 0.2114 2023-03-03 18:37:45,588 - mmseg - INFO - Iter [52550/80000] lr: 4.687e-06, eta: 1:42:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2078, decode.acc_seg: 91.7158, loss: 0.2078 2023-03-03 18:37:55,989 - mmseg - INFO - Iter [52600/80000] lr: 4.687e-06, eta: 1:42:02, time: 0.208, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2077, decode.acc_seg: 91.6253, loss: 0.2077 2023-03-03 18:38:06,265 - mmseg - INFO - Iter [52650/80000] lr: 4.687e-06, eta: 1:41:50, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2098, decode.acc_seg: 91.5587, loss: 0.2098 2023-03-03 18:38:16,270 - mmseg - INFO - Iter [52700/80000] lr: 4.687e-06, eta: 1:41:38, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.4285, loss: 0.2149 2023-03-03 18:38:26,305 - mmseg - INFO - Iter [52750/80000] lr: 4.687e-06, eta: 1:41:27, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2216, decode.acc_seg: 91.2488, loss: 0.2216 2023-03-03 18:38:36,234 - mmseg - INFO - Iter [52800/80000] lr: 4.687e-06, eta: 1:41:15, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2053, decode.acc_seg: 91.7211, loss: 0.2053 2023-03-03 18:38:46,337 - mmseg - INFO - Iter [52850/80000] lr: 4.687e-06, eta: 1:41:03, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2037, decode.acc_seg: 91.7726, loss: 0.2037 2023-03-03 18:38:56,399 - mmseg - INFO - Iter [52900/80000] lr: 4.687e-06, eta: 1:40:51, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2052, decode.acc_seg: 91.7772, loss: 0.2052 2023-03-03 18:39:06,467 - mmseg - INFO - Iter [52950/80000] lr: 4.687e-06, eta: 1:40:40, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.5722, loss: 0.2127 2023-03-03 18:39:16,646 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:39:16,646 - mmseg - INFO - Iter [53000/80000] lr: 4.687e-06, eta: 1:40:28, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2079, decode.acc_seg: 91.7130, loss: 0.2079 2023-03-03 18:39:29,243 - mmseg - INFO - Iter [53050/80000] lr: 4.687e-06, eta: 1:40:18, time: 0.252, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.4427, loss: 0.2122 2023-03-03 18:39:39,189 - mmseg - INFO - Iter [53100/80000] lr: 4.687e-06, eta: 1:40:06, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.6673, loss: 0.2091 2023-03-03 18:39:49,182 - mmseg - INFO - Iter [53150/80000] lr: 4.687e-06, eta: 1:39:54, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2065, decode.acc_seg: 91.7966, loss: 0.2065 2023-03-03 18:39:59,092 - mmseg - INFO - Iter [53200/80000] lr: 4.687e-06, eta: 1:39:42, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2029, decode.acc_seg: 91.8198, loss: 0.2029 2023-03-03 18:40:09,005 - mmseg - INFO - Iter [53250/80000] lr: 4.687e-06, eta: 1:39:30, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2064, decode.acc_seg: 91.6482, loss: 0.2064 2023-03-03 18:40:18,845 - mmseg - INFO - Iter [53300/80000] lr: 4.687e-06, eta: 1:39:19, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2154, decode.acc_seg: 91.4032, loss: 0.2154 2023-03-03 18:40:29,029 - mmseg - INFO - Iter [53350/80000] lr: 4.687e-06, eta: 1:39:07, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2137, decode.acc_seg: 91.4531, loss: 0.2137 2023-03-03 18:40:39,077 - mmseg - INFO - Iter [53400/80000] lr: 4.687e-06, eta: 1:38:55, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.4085, loss: 0.2117 2023-03-03 18:40:49,080 - mmseg - INFO - Iter [53450/80000] lr: 4.687e-06, eta: 1:38:43, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2226, decode.acc_seg: 91.1920, loss: 0.2226 2023-03-03 18:40:59,044 - mmseg - INFO - Iter [53500/80000] lr: 4.687e-06, eta: 1:38:32, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.5423, loss: 0.2090 2023-03-03 18:41:09,050 - mmseg - INFO - Iter [53550/80000] lr: 4.687e-06, eta: 1:38:20, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2192, decode.acc_seg: 91.2317, loss: 0.2192 2023-03-03 18:41:18,996 - mmseg - INFO - Iter [53600/80000] lr: 4.687e-06, eta: 1:38:08, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2146, decode.acc_seg: 91.4831, loss: 0.2146 2023-03-03 18:41:31,455 - mmseg - INFO - Iter [53650/80000] lr: 4.687e-06, eta: 1:37:58, time: 0.249, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2069, decode.acc_seg: 91.9046, loss: 0.2069 2023-03-03 18:41:41,378 - mmseg - INFO - Iter [53700/80000] lr: 4.687e-06, eta: 1:37:46, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2054, decode.acc_seg: 91.7644, loss: 0.2054 2023-03-03 18:41:51,463 - mmseg - INFO - Iter [53750/80000] lr: 4.687e-06, eta: 1:37:34, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2106, decode.acc_seg: 91.5661, loss: 0.2106 2023-03-03 18:42:01,565 - mmseg - INFO - Iter [53800/80000] lr: 4.687e-06, eta: 1:37:23, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2098, decode.acc_seg: 91.5342, loss: 0.2098 2023-03-03 18:42:11,664 - mmseg - INFO - Iter [53850/80000] lr: 4.687e-06, eta: 1:37:11, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2045, decode.acc_seg: 91.8377, loss: 0.2045 2023-03-03 18:42:21,578 - mmseg - INFO - Iter [53900/80000] lr: 4.687e-06, eta: 1:36:59, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.6937, loss: 0.2110 2023-03-03 18:42:31,623 - mmseg - INFO - Iter [53950/80000] lr: 4.687e-06, eta: 1:36:48, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2184, decode.acc_seg: 91.3290, loss: 0.2184 2023-03-03 18:42:41,614 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:42:41,614 - mmseg - INFO - Iter [54000/80000] lr: 4.687e-06, eta: 1:36:36, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2100, decode.acc_seg: 91.6233, loss: 0.2100 2023-03-03 18:42:51,703 - mmseg - INFO - Iter [54050/80000] lr: 4.687e-06, eta: 1:36:24, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.5824, loss: 0.2081 2023-03-03 18:43:01,721 - mmseg - INFO - Iter [54100/80000] lr: 4.687e-06, eta: 1:36:13, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2174, decode.acc_seg: 91.2803, loss: 0.2174 2023-03-03 18:43:11,632 - mmseg - INFO - Iter [54150/80000] lr: 4.687e-06, eta: 1:36:01, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2136, decode.acc_seg: 91.5702, loss: 0.2136 2023-03-03 18:43:21,663 - mmseg - INFO - Iter [54200/80000] lr: 4.687e-06, eta: 1:35:49, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.6503, loss: 0.2128 2023-03-03 18:43:31,607 - mmseg - INFO - Iter [54250/80000] lr: 4.687e-06, eta: 1:35:37, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2038, decode.acc_seg: 91.8386, loss: 0.2038 2023-03-03 18:43:44,247 - mmseg - INFO - Iter [54300/80000] lr: 4.687e-06, eta: 1:35:27, time: 0.253, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.5291, loss: 0.2110 2023-03-03 18:43:54,521 - mmseg - INFO - Iter [54350/80000] lr: 4.687e-06, eta: 1:35:15, time: 0.205, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2058, decode.acc_seg: 91.8289, loss: 0.2058 2023-03-03 18:44:04,567 - mmseg - INFO - Iter [54400/80000] lr: 4.687e-06, eta: 1:35:04, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2106, decode.acc_seg: 91.6641, loss: 0.2106 2023-03-03 18:44:14,577 - mmseg - INFO - Iter [54450/80000] lr: 4.687e-06, eta: 1:34:52, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.4572, loss: 0.2153 2023-03-03 18:44:24,668 - mmseg - INFO - Iter [54500/80000] lr: 4.687e-06, eta: 1:34:40, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2138, decode.acc_seg: 91.4072, loss: 0.2138 2023-03-03 18:44:34,726 - mmseg - INFO - Iter [54550/80000] lr: 4.687e-06, eta: 1:34:29, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.3786, loss: 0.2115 2023-03-03 18:44:44,687 - mmseg - INFO - Iter [54600/80000] lr: 4.687e-06, eta: 1:34:17, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2029, decode.acc_seg: 91.8926, loss: 0.2029 2023-03-03 18:44:54,675 - mmseg - INFO - Iter [54650/80000] lr: 4.687e-06, eta: 1:34:05, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.5777, loss: 0.2080 2023-03-03 18:45:04,636 - mmseg - INFO - Iter [54700/80000] lr: 4.687e-06, eta: 1:33:54, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.3322, loss: 0.2149 2023-03-03 18:45:14,554 - mmseg - INFO - Iter [54750/80000] lr: 4.687e-06, eta: 1:33:42, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.5351, loss: 0.2117 2023-03-03 18:45:24,810 - mmseg - INFO - Iter [54800/80000] lr: 4.687e-06, eta: 1:33:31, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2075, decode.acc_seg: 91.7189, loss: 0.2075 2023-03-03 18:45:34,747 - mmseg - INFO - Iter [54850/80000] lr: 4.687e-06, eta: 1:33:19, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2061, decode.acc_seg: 91.7874, loss: 0.2061 2023-03-03 18:45:47,160 - mmseg - INFO - Iter [54900/80000] lr: 4.687e-06, eta: 1:33:08, time: 0.248, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2083, decode.acc_seg: 91.6395, loss: 0.2083 2023-03-03 18:45:57,247 - mmseg - INFO - Iter [54950/80000] lr: 4.687e-06, eta: 1:32:57, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2132, decode.acc_seg: 91.4849, loss: 0.2132 2023-03-03 18:46:07,152 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:46:07,152 - mmseg - INFO - Iter [55000/80000] lr: 4.687e-06, eta: 1:32:45, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.6753, loss: 0.2091 2023-03-03 18:46:17,111 - mmseg - INFO - Iter [55050/80000] lr: 4.687e-06, eta: 1:32:33, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2095, decode.acc_seg: 91.6508, loss: 0.2095 2023-03-03 18:46:27,148 - mmseg - INFO - Iter [55100/80000] lr: 4.687e-06, eta: 1:32:22, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2079, decode.acc_seg: 91.7871, loss: 0.2079 2023-03-03 18:46:37,104 - mmseg - INFO - Iter [55150/80000] lr: 4.687e-06, eta: 1:32:10, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2075, decode.acc_seg: 91.7214, loss: 0.2075 2023-03-03 18:46:47,146 - mmseg - INFO - Iter [55200/80000] lr: 4.687e-06, eta: 1:31:58, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2043, decode.acc_seg: 91.7828, loss: 0.2043 2023-03-03 18:46:57,230 - mmseg - INFO - Iter [55250/80000] lr: 4.687e-06, eta: 1:31:47, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2078, decode.acc_seg: 91.5361, loss: 0.2078 2023-03-03 18:47:07,194 - mmseg - INFO - Iter [55300/80000] lr: 4.687e-06, eta: 1:31:35, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.4333, loss: 0.2122 2023-03-03 18:47:17,079 - mmseg - INFO - Iter [55350/80000] lr: 4.687e-06, eta: 1:31:24, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2093, decode.acc_seg: 91.6426, loss: 0.2093 2023-03-03 18:47:27,274 - mmseg - INFO - Iter [55400/80000] lr: 4.687e-06, eta: 1:31:12, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.4757, loss: 0.2143 2023-03-03 18:47:37,392 - mmseg - INFO - Iter [55450/80000] lr: 4.687e-06, eta: 1:31:00, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2045, decode.acc_seg: 91.7330, loss: 0.2045 2023-03-03 18:47:47,392 - mmseg - INFO - Iter [55500/80000] lr: 4.687e-06, eta: 1:30:49, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2103, decode.acc_seg: 91.5811, loss: 0.2103 2023-03-03 18:47:59,851 - mmseg - INFO - Iter [55550/80000] lr: 4.687e-06, eta: 1:30:38, time: 0.249, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2005, decode.acc_seg: 91.8517, loss: 0.2005 2023-03-03 18:48:10,108 - mmseg - INFO - Iter [55600/80000] lr: 4.687e-06, eta: 1:30:27, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.6024, loss: 0.2122 2023-03-03 18:48:20,126 - mmseg - INFO - Iter [55650/80000] lr: 4.687e-06, eta: 1:30:15, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2104, decode.acc_seg: 91.4676, loss: 0.2104 2023-03-03 18:48:30,325 - mmseg - INFO - Iter [55700/80000] lr: 4.687e-06, eta: 1:30:04, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2213, decode.acc_seg: 91.0978, loss: 0.2213 2023-03-03 18:48:40,279 - mmseg - INFO - Iter [55750/80000] lr: 4.687e-06, eta: 1:29:52, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2056, decode.acc_seg: 91.7804, loss: 0.2056 2023-03-03 18:48:50,254 - mmseg - INFO - Iter [55800/80000] lr: 4.687e-06, eta: 1:29:40, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2186, decode.acc_seg: 91.3435, loss: 0.2186 2023-03-03 18:49:00,300 - mmseg - INFO - Iter [55850/80000] lr: 4.687e-06, eta: 1:29:29, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.6974, loss: 0.2125 2023-03-03 18:49:10,189 - mmseg - INFO - Iter [55900/80000] lr: 4.687e-06, eta: 1:29:17, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2034, decode.acc_seg: 91.7011, loss: 0.2034 2023-03-03 18:49:20,079 - mmseg - INFO - Iter [55950/80000] lr: 4.687e-06, eta: 1:29:06, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2104, decode.acc_seg: 91.5354, loss: 0.2104 2023-03-03 18:49:29,995 - mmseg - INFO - Saving checkpoint at 56000 iterations 2023-03-03 18:49:30,932 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:49:30,932 - mmseg - INFO - Iter [56000/80000] lr: 4.687e-06, eta: 1:28:54, time: 0.217, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.5646, loss: 0.2124 2023-03-03 18:49:45,724 - mmseg - INFO - per class results: 2023-03-03 18:49:45,730 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 76.13 | 88.01 | | building | 82.5 | 93.48 | | sky | 94.12 | 97.16 | | floor | 78.91 | 90.72 | | tree | 73.3 | 87.23 | | ceiling | 82.33 | 91.32 | | road | 81.1 | 89.43 | | bed | 87.73 | 95.47 | | windowpane | 59.64 | 76.36 | | grass | 65.29 | 82.48 | | cabinet | 58.67 | 73.27 | | sidewalk | 64.86 | 80.36 | | person | 77.91 | 91.24 | | earth | 31.74 | 44.61 | | door | 45.96 | 60.53 | | table | 59.72 | 75.41 | | mountain | 51.2 | 68.81 | | plant | 51.21 | 64.69 | | curtain | 71.47 | 83.02 | | chair | 54.59 | 68.56 | | car | 81.35 | 90.57 | | water | 45.47 | 60.54 | | painting | 70.82 | 85.94 | | sofa | 63.35 | 81.76 | | shelf | 38.3 | 54.36 | | house | 47.18 | 57.18 | | sea | 43.13 | 69.47 | | mirror | 63.52 | 71.64 | | rug | 55.89 | 62.02 | | field | 24.23 | 39.49 | | armchair | 42.16 | 59.61 | | seat | 58.12 | 76.81 | | fence | 34.76 | 46.92 | | desk | 48.84 | 68.2 | | rock | 29.26 | 45.19 | | wardrobe | 46.61 | 61.42 | | lamp | 62.5 | 74.98 | | bathtub | 76.84 | 83.2 | | railing | 28.03 | 40.21 | | cushion | 52.78 | 63.54 | | base | 21.22 | 32.92 | | box | 21.46 | 27.85 | | column | 44.5 | 56.67 | | signboard | 36.22 | 49.22 | | chest of drawers | 37.25 | 57.45 | | counter | 27.85 | 35.57 | | sand | 31.39 | 48.3 | | sink | 66.91 | 78.59 | | skyscraper | 67.21 | 75.56 | | fireplace | 69.16 | 88.17 | | refrigerator | 71.44 | 84.96 | | grandstand | 39.84 | 59.76 | | path | 14.65 | 20.85 | | stairs | 30.43 | 37.1 | | runway | 60.0 | 79.53 | | case | 44.25 | 68.94 | | pool table | 91.32 | 95.91 | | pillow | 54.04 | 65.73 | | screen door | 67.04 | 76.64 | | stairway | 31.33 | 40.36 | | river | 12.31 | 22.82 | | bridge | 64.9 | 71.13 | | bookcase | 39.31 | 51.4 | | blind | 40.31 | 46.38 | | coffee table | 57.99 | 77.74 | | toilet | 85.47 | 90.86 | | flower | 34.46 | 45.55 | | book | 44.54 | 63.23 | | hill | 4.83 | 6.45 | | bench | 37.6 | 48.89 | | countertop | 52.81 | 67.84 | | stove | 72.4 | 81.01 | | palm | 50.96 | 70.3 | | kitchen island | 45.67 | 76.1 | | computer | 55.31 | 65.56 | | swivel chair | 44.95 | 61.88 | | boat | 43.34 | 54.24 | | bar | 25.16 | 30.83 | | arcade machine | 26.38 | 28.98 | | hovel | 38.37 | 42.04 | | bus | 78.43 | 87.23 | | towel | 56.63 | 65.89 | | light | 53.95 | 61.48 | | truck | 32.35 | 44.07 | | tower | 35.79 | 44.88 | | chandelier | 67.99 | 82.67 | | awning | 25.11 | 27.82 | | streetlight | 25.03 | 30.52 | | booth | 41.97 | 48.12 | | television receiver | 66.26 | 78.6 | | airplane | 50.17 | 62.96 | | dirt track | 2.12 | 6.04 | | apparel | 29.62 | 42.35 | | pole | 23.9 | 36.0 | | land | 0.72 | 1.0 | | bannister | 9.77 | 12.15 | | escalator | 21.26 | 22.04 | | ottoman | 42.39 | 54.78 | | bottle | 12.48 | 19.93 | | buffet | 34.47 | 43.72 | | poster | 25.33 | 33.34 | | stage | 6.92 | 8.22 | | van | 41.41 | 56.61 | | ship | 66.96 | 79.99 | | fountain | 0.54 | 0.54 | | conveyer belt | 62.3 | 85.7 | | canopy | 14.69 | 17.18 | | washer | 63.2 | 65.55 | | plaything | 20.12 | 23.61 | | swimming pool | 29.39 | 35.83 | | stool | 40.54 | 50.56 | | barrel | 36.09 | 64.79 | | basket | 23.1 | 32.06 | | waterfall | 59.47 | 80.03 | | tent | 93.25 | 98.26 | | bag | 8.38 | 9.98 | | minibike | 52.95 | 61.61 | | cradle | 76.03 | 97.15 | | oven | 22.48 | 54.08 | | ball | 46.96 | 66.66 | | food | 49.86 | 58.25 | | step | 3.38 | 4.21 | | tank | 46.95 | 47.59 | | trade name | 19.75 | 22.14 | | microwave | 38.44 | 41.19 | | pot | 37.21 | 42.79 | | animal | 49.79 | 51.49 | | bicycle | 46.42 | 67.82 | | lake | 60.83 | 63.02 | | dishwasher | 70.09 | 73.31 | | screen | 59.35 | 70.17 | | blanket | 7.09 | 8.46 | | sculpture | 41.34 | 66.56 | | hood | 59.18 | 69.48 | | sconce | 40.62 | 47.04 | | vase | 31.8 | 46.46 | | traffic light | 26.53 | 37.36 | | tray | 4.41 | 6.2 | | ashcan | 41.47 | 54.66 | | fan | 56.48 | 70.62 | | pier | 21.19 | 28.99 | | crt screen | 4.38 | 8.21 | | plate | 39.73 | 50.84 | | monitor | 63.49 | 72.84 | | bulletin board | 35.64 | 50.39 | | shower | 1.46 | 2.01 | | radiator | 41.12 | 48.97 | | glass | 9.81 | 10.72 | | clock | 18.28 | 22.82 | | flag | 39.77 | 41.78 | +---------------------+-------+-------+ 2023-03-03 18:49:45,730 - mmseg - INFO - Summary: 2023-03-03 18:49:45,731 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.64 | 44.88 | 55.73 | +-------+-------+-------+ 2023-03-03 18:49:45,763 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_48000.pth was removed 2023-03-03 18:49:46,648 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_56000.pth. 2023-03-03 18:49:46,648 - mmseg - INFO - Best mIoU is 0.4488 at 56000 iter. 2023-03-03 18:49:46,648 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:49:46,649 - mmseg - INFO - Iter(val) [250] aAcc: 0.8164, mIoU: 0.4488, mAcc: 0.5573, IoU.background: nan, IoU.wall: 0.7613, IoU.building: 0.8250, IoU.sky: 0.9412, IoU.floor: 0.7891, IoU.tree: 0.7330, IoU.ceiling: 0.8233, IoU.road: 0.8110, IoU.bed : 0.8773, IoU.windowpane: 0.5964, IoU.grass: 0.6529, IoU.cabinet: 0.5867, IoU.sidewalk: 0.6486, IoU.person: 0.7791, IoU.earth: 0.3174, IoU.door: 0.4596, IoU.table: 0.5972, IoU.mountain: 0.5120, IoU.plant: 0.5121, IoU.curtain: 0.7147, IoU.chair: 0.5459, IoU.car: 0.8135, IoU.water: 0.4547, IoU.painting: 0.7082, IoU.sofa: 0.6335, IoU.shelf: 0.3830, IoU.house: 0.4718, IoU.sea: 0.4313, IoU.mirror: 0.6352, IoU.rug: 0.5589, IoU.field: 0.2423, IoU.armchair: 0.4216, IoU.seat: 0.5812, IoU.fence: 0.3476, IoU.desk: 0.4884, IoU.rock: 0.2926, IoU.wardrobe: 0.4661, IoU.lamp: 0.6250, IoU.bathtub: 0.7684, IoU.railing: 0.2803, IoU.cushion: 0.5278, IoU.base: 0.2122, IoU.box: 0.2146, IoU.column: 0.4450, IoU.signboard: 0.3622, IoU.chest of drawers: 0.3725, IoU.counter: 0.2785, IoU.sand: 0.3139, IoU.sink: 0.6691, IoU.skyscraper: 0.6721, IoU.fireplace: 0.6916, IoU.refrigerator: 0.7144, IoU.grandstand: 0.3984, IoU.path: 0.1465, IoU.stairs: 0.3043, IoU.runway: 0.6000, IoU.case: 0.4425, IoU.pool table: 0.9132, IoU.pillow: 0.5404, IoU.screen door: 0.6704, IoU.stairway: 0.3133, IoU.river: 0.1231, IoU.bridge: 0.6490, IoU.bookcase: 0.3931, IoU.blind: 0.4031, IoU.coffee table: 0.5799, IoU.toilet: 0.8547, IoU.flower: 0.3446, IoU.book: 0.4454, IoU.hill: 0.0483, IoU.bench: 0.3760, IoU.countertop: 0.5281, IoU.stove: 0.7240, IoU.palm: 0.5096, IoU.kitchen island: 0.4567, IoU.computer: 0.5531, IoU.swivel chair: 0.4495, IoU.boat: 0.4334, IoU.bar: 0.2516, IoU.arcade machine: 0.2638, IoU.hovel: 0.3837, IoU.bus: 0.7843, IoU.towel: 0.5663, IoU.light: 0.5395, IoU.truck: 0.3235, IoU.tower: 0.3579, IoU.chandelier: 0.6799, IoU.awning: 0.2511, IoU.streetlight: 0.2503, IoU.booth: 0.4197, IoU.television receiver: 0.6626, IoU.airplane: 0.5017, IoU.dirt track: 0.0212, IoU.apparel: 0.2962, IoU.pole: 0.2390, IoU.land: 0.0072, IoU.bannister: 0.0977, IoU.escalator: 0.2126, IoU.ottoman: 0.4239, IoU.bottle: 0.1248, IoU.buffet: 0.3447, IoU.poster: 0.2533, IoU.stage: 0.0692, IoU.van: 0.4141, IoU.ship: 0.6696, IoU.fountain: 0.0054, IoU.conveyer belt: 0.6230, IoU.canopy: 0.1469, IoU.washer: 0.6320, IoU.plaything: 0.2012, IoU.swimming pool: 0.2939, IoU.stool: 0.4054, IoU.barrel: 0.3609, IoU.basket: 0.2310, IoU.waterfall: 0.5947, IoU.tent: 0.9325, IoU.bag: 0.0838, IoU.minibike: 0.5295, IoU.cradle: 0.7603, IoU.oven: 0.2248, IoU.ball: 0.4696, IoU.food: 0.4986, IoU.step: 0.0338, IoU.tank: 0.4695, IoU.trade name: 0.1975, IoU.microwave: 0.3844, IoU.pot: 0.3721, IoU.animal: 0.4979, IoU.bicycle: 0.4642, IoU.lake: 0.6083, IoU.dishwasher: 0.7009, IoU.screen: 0.5935, IoU.blanket: 0.0709, IoU.sculpture: 0.4134, IoU.hood: 0.5918, IoU.sconce: 0.4062, IoU.vase: 0.3180, IoU.traffic light: 0.2653, IoU.tray: 0.0441, IoU.ashcan: 0.4147, IoU.fan: 0.5648, IoU.pier: 0.2119, IoU.crt screen: 0.0438, IoU.plate: 0.3973, IoU.monitor: 0.6349, IoU.bulletin board: 0.3564, IoU.shower: 0.0146, IoU.radiator: 0.4112, IoU.glass: 0.0981, IoU.clock: 0.1828, IoU.flag: 0.3977, Acc.background: nan, Acc.wall: 0.8801, Acc.building: 0.9348, Acc.sky: 0.9716, Acc.floor: 0.9072, Acc.tree: 0.8723, Acc.ceiling: 0.9132, Acc.road: 0.8943, Acc.bed : 0.9547, Acc.windowpane: 0.7636, Acc.grass: 0.8248, Acc.cabinet: 0.7327, Acc.sidewalk: 0.8036, Acc.person: 0.9124, Acc.earth: 0.4461, Acc.door: 0.6053, Acc.table: 0.7541, Acc.mountain: 0.6881, Acc.plant: 0.6469, Acc.curtain: 0.8302, Acc.chair: 0.6856, Acc.car: 0.9057, Acc.water: 0.6054, Acc.painting: 0.8594, Acc.sofa: 0.8176, Acc.shelf: 0.5436, Acc.house: 0.5718, Acc.sea: 0.6947, Acc.mirror: 0.7164, Acc.rug: 0.6202, Acc.field: 0.3949, Acc.armchair: 0.5961, Acc.seat: 0.7681, Acc.fence: 0.4692, Acc.desk: 0.6820, Acc.rock: 0.4519, Acc.wardrobe: 0.6142, Acc.lamp: 0.7498, Acc.bathtub: 0.8320, Acc.railing: 0.4021, Acc.cushion: 0.6354, Acc.base: 0.3292, Acc.box: 0.2785, Acc.column: 0.5667, Acc.signboard: 0.4922, Acc.chest of drawers: 0.5745, Acc.counter: 0.3557, Acc.sand: 0.4830, Acc.sink: 0.7859, Acc.skyscraper: 0.7556, Acc.fireplace: 0.8817, Acc.refrigerator: 0.8496, Acc.grandstand: 0.5976, Acc.path: 0.2085, Acc.stairs: 0.3710, Acc.runway: 0.7953, Acc.case: 0.6894, Acc.pool table: 0.9591, Acc.pillow: 0.6573, Acc.screen door: 0.7664, Acc.stairway: 0.4036, Acc.river: 0.2282, Acc.bridge: 0.7113, Acc.bookcase: 0.5140, Acc.blind: 0.4638, Acc.coffee table: 0.7774, Acc.toilet: 0.9086, Acc.flower: 0.4555, Acc.book: 0.6323, Acc.hill: 0.0645, Acc.bench: 0.4889, Acc.countertop: 0.6784, Acc.stove: 0.8101, Acc.palm: 0.7030, Acc.kitchen island: 0.7610, Acc.computer: 0.6556, Acc.swivel chair: 0.6188, Acc.boat: 0.5424, Acc.bar: 0.3083, Acc.arcade machine: 0.2898, Acc.hovel: 0.4204, Acc.bus: 0.8723, Acc.towel: 0.6589, Acc.light: 0.6148, Acc.truck: 0.4407, Acc.tower: 0.4488, Acc.chandelier: 0.8267, Acc.awning: 0.2782, Acc.streetlight: 0.3052, Acc.booth: 0.4812, Acc.television receiver: 0.7860, Acc.airplane: 0.6296, Acc.dirt track: 0.0604, Acc.apparel: 0.4235, Acc.pole: 0.3600, Acc.land: 0.0100, Acc.bannister: 0.1215, Acc.escalator: 0.2204, Acc.ottoman: 0.5478, Acc.bottle: 0.1993, Acc.buffet: 0.4372, Acc.poster: 0.3334, Acc.stage: 0.0822, Acc.van: 0.5661, Acc.ship: 0.7999, Acc.fountain: 0.0054, Acc.conveyer belt: 0.8570, Acc.canopy: 0.1718, Acc.washer: 0.6555, Acc.plaything: 0.2361, Acc.swimming pool: 0.3583, Acc.stool: 0.5056, Acc.barrel: 0.6479, Acc.basket: 0.3206, Acc.waterfall: 0.8003, Acc.tent: 0.9826, Acc.bag: 0.0998, Acc.minibike: 0.6161, Acc.cradle: 0.9715, Acc.oven: 0.5408, Acc.ball: 0.6666, Acc.food: 0.5825, Acc.step: 0.0421, Acc.tank: 0.4759, Acc.trade name: 0.2214, Acc.microwave: 0.4119, Acc.pot: 0.4279, Acc.animal: 0.5149, Acc.bicycle: 0.6782, Acc.lake: 0.6302, Acc.dishwasher: 0.7331, Acc.screen: 0.7017, Acc.blanket: 0.0846, Acc.sculpture: 0.6656, Acc.hood: 0.6948, Acc.sconce: 0.4704, Acc.vase: 0.4646, Acc.traffic light: 0.3736, Acc.tray: 0.0620, Acc.ashcan: 0.5466, Acc.fan: 0.7062, Acc.pier: 0.2899, Acc.crt screen: 0.0821, Acc.plate: 0.5084, Acc.monitor: 0.7284, Acc.bulletin board: 0.5039, Acc.shower: 0.0201, Acc.radiator: 0.4897, Acc.glass: 0.1072, Acc.clock: 0.2282, Acc.flag: 0.4178 2023-03-03 18:49:57,020 - mmseg - INFO - Iter [56050/80000] lr: 4.687e-06, eta: 1:28:50, time: 0.522, data_time: 0.322, memory: 67202, decode.loss_ce: 0.2025, decode.acc_seg: 91.6271, loss: 0.2025 2023-03-03 18:50:07,249 - mmseg - INFO - Iter [56100/80000] lr: 4.687e-06, eta: 1:28:38, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2037, decode.acc_seg: 91.9157, loss: 0.2037 2023-03-03 18:50:17,492 - mmseg - INFO - Iter [56150/80000] lr: 4.687e-06, eta: 1:28:27, time: 0.205, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.5289, loss: 0.2140 2023-03-03 18:50:30,183 - mmseg - INFO - Iter [56200/80000] lr: 4.687e-06, eta: 1:28:16, time: 0.254, data_time: 0.058, memory: 67202, decode.loss_ce: 0.2136, decode.acc_seg: 91.5378, loss: 0.2136 2023-03-03 18:50:40,374 - mmseg - INFO - Iter [56250/80000] lr: 4.687e-06, eta: 1:28:05, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2070, decode.acc_seg: 91.8330, loss: 0.2070 2023-03-03 18:50:50,425 - mmseg - INFO - Iter [56300/80000] lr: 4.687e-06, eta: 1:27:53, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2048, decode.acc_seg: 91.8041, loss: 0.2048 2023-03-03 18:51:00,464 - mmseg - INFO - Iter [56350/80000] lr: 4.687e-06, eta: 1:27:41, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.5800, loss: 0.2127 2023-03-03 18:51:10,641 - mmseg - INFO - Iter [56400/80000] lr: 4.687e-06, eta: 1:27:30, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2038, decode.acc_seg: 91.7301, loss: 0.2038 2023-03-03 18:51:20,987 - mmseg - INFO - Iter [56450/80000] lr: 4.687e-06, eta: 1:27:18, time: 0.207, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2203, decode.acc_seg: 91.0751, loss: 0.2203 2023-03-03 18:51:30,933 - mmseg - INFO - Iter [56500/80000] lr: 4.687e-06, eta: 1:27:07, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2109, decode.acc_seg: 91.6200, loss: 0.2109 2023-03-03 18:51:41,031 - mmseg - INFO - Iter [56550/80000] lr: 4.687e-06, eta: 1:26:55, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.6274, loss: 0.2091 2023-03-03 18:51:50,899 - mmseg - INFO - Iter [56600/80000] lr: 4.687e-06, eta: 1:26:44, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2088, decode.acc_seg: 91.7124, loss: 0.2088 2023-03-03 18:52:00,859 - mmseg - INFO - Iter [56650/80000] lr: 4.687e-06, eta: 1:26:32, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.4606, loss: 0.2143 2023-03-03 18:52:10,833 - mmseg - INFO - Iter [56700/80000] lr: 4.687e-06, eta: 1:26:20, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2076, decode.acc_seg: 91.5567, loss: 0.2076 2023-03-03 18:52:20,840 - mmseg - INFO - Iter [56750/80000] lr: 4.687e-06, eta: 1:26:09, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.7057, loss: 0.2091 2023-03-03 18:52:33,331 - mmseg - INFO - Iter [56800/80000] lr: 4.687e-06, eta: 1:25:58, time: 0.250, data_time: 0.052, memory: 67202, decode.loss_ce: 0.2077, decode.acc_seg: 91.5636, loss: 0.2077 2023-03-03 18:52:43,332 - mmseg - INFO - Iter [56850/80000] lr: 4.687e-06, eta: 1:25:47, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2107, decode.acc_seg: 91.5494, loss: 0.2107 2023-03-03 18:52:53,459 - mmseg - INFO - Iter [56900/80000] lr: 4.687e-06, eta: 1:25:35, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2039, decode.acc_seg: 91.7503, loss: 0.2039 2023-03-03 18:53:03,383 - mmseg - INFO - Iter [56950/80000] lr: 4.687e-06, eta: 1:25:24, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.4917, loss: 0.2127 2023-03-03 18:53:13,260 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:53:13,260 - mmseg - INFO - Iter [57000/80000] lr: 4.687e-06, eta: 1:25:12, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2057, decode.acc_seg: 91.6811, loss: 0.2057 2023-03-03 18:53:23,165 - mmseg - INFO - Iter [57050/80000] lr: 4.687e-06, eta: 1:25:00, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2111, decode.acc_seg: 91.6477, loss: 0.2111 2023-03-03 18:53:33,146 - mmseg - INFO - Iter [57100/80000] lr: 4.687e-06, eta: 1:24:49, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2164, decode.acc_seg: 91.5078, loss: 0.2164 2023-03-03 18:53:43,065 - mmseg - INFO - Iter [57150/80000] lr: 4.687e-06, eta: 1:24:37, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2157, decode.acc_seg: 91.3723, loss: 0.2157 2023-03-03 18:53:52,925 - mmseg - INFO - Iter [57200/80000] lr: 4.687e-06, eta: 1:24:26, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2077, decode.acc_seg: 91.8007, loss: 0.2077 2023-03-03 18:54:03,004 - mmseg - INFO - Iter [57250/80000] lr: 4.687e-06, eta: 1:24:14, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2078, decode.acc_seg: 91.7373, loss: 0.2078 2023-03-03 18:54:13,083 - mmseg - INFO - Iter [57300/80000] lr: 4.687e-06, eta: 1:24:03, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2237, decode.acc_seg: 91.1303, loss: 0.2237 2023-03-03 18:54:23,327 - mmseg - INFO - Iter [57350/80000] lr: 4.687e-06, eta: 1:23:51, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.5268, loss: 0.2151 2023-03-03 18:54:33,496 - mmseg - INFO - Iter [57400/80000] lr: 4.687e-06, eta: 1:23:40, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.3983, loss: 0.2140 2023-03-03 18:54:45,854 - mmseg - INFO - Iter [57450/80000] lr: 4.687e-06, eta: 1:23:29, time: 0.247, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2069, decode.acc_seg: 91.7071, loss: 0.2069 2023-03-03 18:54:55,908 - mmseg - INFO - Iter [57500/80000] lr: 4.687e-06, eta: 1:23:18, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.4489, loss: 0.2145 2023-03-03 18:55:06,034 - mmseg - INFO - Iter [57550/80000] lr: 4.687e-06, eta: 1:23:06, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.5036, loss: 0.2122 2023-03-03 18:55:15,969 - mmseg - INFO - Iter [57600/80000] lr: 4.687e-06, eta: 1:22:55, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2083, decode.acc_seg: 91.4634, loss: 0.2083 2023-03-03 18:55:25,890 - mmseg - INFO - Iter [57650/80000] lr: 4.687e-06, eta: 1:22:43, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2082, decode.acc_seg: 91.6949, loss: 0.2082 2023-03-03 18:55:35,899 - mmseg - INFO - Iter [57700/80000] lr: 4.687e-06, eta: 1:22:31, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2161, decode.acc_seg: 91.3461, loss: 0.2161 2023-03-03 18:55:45,827 - mmseg - INFO - Iter [57750/80000] lr: 4.687e-06, eta: 1:22:20, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2152, decode.acc_seg: 91.4117, loss: 0.2152 2023-03-03 18:55:55,720 - mmseg - INFO - Iter [57800/80000] lr: 4.687e-06, eta: 1:22:08, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2174, decode.acc_seg: 91.4498, loss: 0.2174 2023-03-03 18:56:05,747 - mmseg - INFO - Iter [57850/80000] lr: 4.687e-06, eta: 1:21:57, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.7292, loss: 0.2090 2023-03-03 18:56:15,649 - mmseg - INFO - Iter [57900/80000] lr: 4.687e-06, eta: 1:21:45, time: 0.198, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.5515, loss: 0.2125 2023-03-03 18:56:25,700 - mmseg - INFO - Iter [57950/80000] lr: 4.687e-06, eta: 1:21:34, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.8152, loss: 0.2060 2023-03-03 18:56:35,976 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 18:56:35,976 - mmseg - INFO - Iter [58000/80000] lr: 4.687e-06, eta: 1:21:22, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2036, decode.acc_seg: 91.8914, loss: 0.2036 2023-03-03 18:56:46,028 - mmseg - INFO - Iter [58050/80000] lr: 4.687e-06, eta: 1:21:11, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.2973, loss: 0.2150 2023-03-03 18:56:58,747 - mmseg - INFO - Iter [58100/80000] lr: 4.687e-06, eta: 1:21:00, time: 0.254, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2175, decode.acc_seg: 91.3625, loss: 0.2175 2023-03-03 18:57:08,973 - mmseg - INFO - Iter [58150/80000] lr: 4.687e-06, eta: 1:20:49, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.5502, loss: 0.2153 2023-03-03 18:57:18,904 - mmseg - INFO - Iter [58200/80000] lr: 4.687e-06, eta: 1:20:37, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2010, decode.acc_seg: 91.9267, loss: 0.2010 2023-03-03 18:57:28,943 - mmseg - INFO - Iter [58250/80000] lr: 4.687e-06, eta: 1:20:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.6014, loss: 0.2105 2023-03-03 18:57:38,939 - mmseg - INFO - Iter [58300/80000] lr: 4.687e-06, eta: 1:20:14, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2061, decode.acc_seg: 91.6937, loss: 0.2061 2023-03-03 18:57:48,969 - mmseg - INFO - Iter [58350/80000] lr: 4.687e-06, eta: 1:20:03, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2054, decode.acc_seg: 91.7522, loss: 0.2054 2023-03-03 18:57:59,210 - mmseg - INFO - Iter [58400/80000] lr: 4.687e-06, eta: 1:19:52, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2052, decode.acc_seg: 91.5422, loss: 0.2052 2023-03-03 18:58:09,155 - mmseg - INFO - Iter [58450/80000] lr: 4.687e-06, eta: 1:19:40, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2219, decode.acc_seg: 91.1213, loss: 0.2219 2023-03-03 18:58:19,611 - mmseg - INFO - Iter [58500/80000] lr: 4.687e-06, eta: 1:19:29, time: 0.209, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2048, decode.acc_seg: 91.7738, loss: 0.2048 2023-03-03 18:58:29,754 - mmseg - INFO - Iter [58550/80000] lr: 4.687e-06, eta: 1:19:17, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2063, decode.acc_seg: 91.6360, loss: 0.2063 2023-03-03 18:58:39,780 - mmseg - INFO - Iter [58600/80000] lr: 4.687e-06, eta: 1:19:06, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.4064, loss: 0.2181 2023-03-03 18:58:49,807 - mmseg - INFO - Iter [58650/80000] lr: 4.687e-06, eta: 1:18:54, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.5991, loss: 0.2108 2023-03-03 18:59:02,312 - mmseg - INFO - Iter [58700/80000] lr: 4.687e-06, eta: 1:18:44, time: 0.250, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.5167, loss: 0.2127 2023-03-03 18:59:12,306 - mmseg - INFO - Iter [58750/80000] lr: 4.687e-06, eta: 1:18:32, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2026, decode.acc_seg: 91.7331, loss: 0.2026 2023-03-03 18:59:22,256 - mmseg - INFO - Iter [58800/80000] lr: 4.687e-06, eta: 1:18:21, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2116, decode.acc_seg: 91.5378, loss: 0.2116 2023-03-03 18:59:32,228 - mmseg - INFO - Iter [58850/80000] lr: 4.687e-06, eta: 1:18:09, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2136, decode.acc_seg: 91.6677, loss: 0.2136 2023-03-03 18:59:42,224 - mmseg - INFO - Iter [58900/80000] lr: 4.687e-06, eta: 1:17:58, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2111, decode.acc_seg: 91.5110, loss: 0.2111 2023-03-03 18:59:52,202 - mmseg - INFO - Iter [58950/80000] lr: 4.687e-06, eta: 1:17:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2114, decode.acc_seg: 91.5676, loss: 0.2114 2023-03-03 19:00:02,185 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:00:02,186 - mmseg - INFO - Iter [59000/80000] lr: 4.687e-06, eta: 1:17:35, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.5488, loss: 0.2092 2023-03-03 19:00:12,107 - mmseg - INFO - Iter [59050/80000] lr: 4.687e-06, eta: 1:17:23, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2204, decode.acc_seg: 91.1856, loss: 0.2204 2023-03-03 19:00:22,029 - mmseg - INFO - Iter [59100/80000] lr: 4.687e-06, eta: 1:17:12, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2079, decode.acc_seg: 91.7122, loss: 0.2079 2023-03-03 19:00:31,919 - mmseg - INFO - Iter [59150/80000] lr: 4.687e-06, eta: 1:17:00, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2028, decode.acc_seg: 91.7478, loss: 0.2028 2023-03-03 19:00:42,114 - mmseg - INFO - Iter [59200/80000] lr: 4.687e-06, eta: 1:16:49, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2121, decode.acc_seg: 91.5661, loss: 0.2121 2023-03-03 19:00:52,023 - mmseg - INFO - Iter [59250/80000] lr: 4.687e-06, eta: 1:16:37, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2123, decode.acc_seg: 91.5726, loss: 0.2123 2023-03-03 19:01:02,156 - mmseg - INFO - Iter [59300/80000] lr: 4.687e-06, eta: 1:16:26, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2236, decode.acc_seg: 91.3090, loss: 0.2236 2023-03-03 19:01:14,825 - mmseg - INFO - Iter [59350/80000] lr: 4.687e-06, eta: 1:16:15, time: 0.253, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2111, decode.acc_seg: 91.5569, loss: 0.2111 2023-03-03 19:01:25,062 - mmseg - INFO - Iter [59400/80000] lr: 4.687e-06, eta: 1:16:04, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1979, decode.acc_seg: 91.9860, loss: 0.1979 2023-03-03 19:01:34,954 - mmseg - INFO - Iter [59450/80000] lr: 4.687e-06, eta: 1:15:53, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2073, decode.acc_seg: 91.6195, loss: 0.2073 2023-03-03 19:01:44,878 - mmseg - INFO - Iter [59500/80000] lr: 4.687e-06, eta: 1:15:41, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2139, decode.acc_seg: 91.4307, loss: 0.2139 2023-03-03 19:01:54,991 - mmseg - INFO - Iter [59550/80000] lr: 4.687e-06, eta: 1:15:30, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.5533, loss: 0.2124 2023-03-03 19:02:04,892 - mmseg - INFO - Iter [59600/80000] lr: 4.687e-06, eta: 1:15:18, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2093, decode.acc_seg: 91.6138, loss: 0.2093 2023-03-03 19:02:14,775 - mmseg - INFO - Iter [59650/80000] lr: 4.687e-06, eta: 1:15:07, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2050, decode.acc_seg: 91.7730, loss: 0.2050 2023-03-03 19:02:24,693 - mmseg - INFO - Iter [59700/80000] lr: 4.687e-06, eta: 1:14:55, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2085, decode.acc_seg: 91.5259, loss: 0.2085 2023-03-03 19:02:34,759 - mmseg - INFO - Iter [59750/80000] lr: 4.687e-06, eta: 1:14:44, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2077, decode.acc_seg: 91.6809, loss: 0.2077 2023-03-03 19:02:44,748 - mmseg - INFO - Iter [59800/80000] lr: 4.687e-06, eta: 1:14:32, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.4540, loss: 0.2128 2023-03-03 19:02:54,678 - mmseg - INFO - Iter [59850/80000] lr: 4.687e-06, eta: 1:14:21, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2083, decode.acc_seg: 91.6208, loss: 0.2083 2023-03-03 19:03:04,692 - mmseg - INFO - Iter [59900/80000] lr: 4.687e-06, eta: 1:14:10, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.5505, loss: 0.2105 2023-03-03 19:03:17,206 - mmseg - INFO - Iter [59950/80000] lr: 4.687e-06, eta: 1:13:59, time: 0.250, data_time: 0.058, memory: 67202, decode.loss_ce: 0.2074, decode.acc_seg: 91.5434, loss: 0.2074 2023-03-03 19:03:27,170 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:03:27,170 - mmseg - INFO - Iter [60000/80000] lr: 4.687e-06, eta: 1:13:48, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.6275, loss: 0.2110 2023-03-03 19:03:37,143 - mmseg - INFO - Iter [60050/80000] lr: 2.344e-06, eta: 1:13:36, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2015, decode.acc_seg: 91.8017, loss: 0.2015 2023-03-03 19:03:47,144 - mmseg - INFO - Iter [60100/80000] lr: 2.344e-06, eta: 1:13:25, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2063, decode.acc_seg: 91.6675, loss: 0.2063 2023-03-03 19:03:57,117 - mmseg - INFO - Iter [60150/80000] lr: 2.344e-06, eta: 1:13:13, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2121, decode.acc_seg: 91.3071, loss: 0.2121 2023-03-03 19:04:07,053 - mmseg - INFO - Iter [60200/80000] lr: 2.344e-06, eta: 1:13:02, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2126, decode.acc_seg: 91.6604, loss: 0.2126 2023-03-03 19:04:17,051 - mmseg - INFO - Iter [60250/80000] lr: 2.344e-06, eta: 1:12:50, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2034, decode.acc_seg: 91.8531, loss: 0.2034 2023-03-03 19:04:27,125 - mmseg - INFO - Iter [60300/80000] lr: 2.344e-06, eta: 1:12:39, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.5585, loss: 0.2124 2023-03-03 19:04:37,203 - mmseg - INFO - Iter [60350/80000] lr: 2.344e-06, eta: 1:12:28, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2193, decode.acc_seg: 91.3057, loss: 0.2193 2023-03-03 19:04:47,304 - mmseg - INFO - Iter [60400/80000] lr: 2.344e-06, eta: 1:12:16, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2201, decode.acc_seg: 91.2763, loss: 0.2201 2023-03-03 19:04:57,407 - mmseg - INFO - Iter [60450/80000] lr: 2.344e-06, eta: 1:12:05, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.4029, loss: 0.2159 2023-03-03 19:05:07,432 - mmseg - INFO - Iter [60500/80000] lr: 2.344e-06, eta: 1:11:53, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2204, decode.acc_seg: 91.2671, loss: 0.2204 2023-03-03 19:05:17,406 - mmseg - INFO - Iter [60550/80000] lr: 2.344e-06, eta: 1:11:42, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2070, decode.acc_seg: 91.7197, loss: 0.2070 2023-03-03 19:05:29,947 - mmseg - INFO - Iter [60600/80000] lr: 2.344e-06, eta: 1:11:31, time: 0.251, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.6941, loss: 0.2092 2023-03-03 19:05:39,848 - mmseg - INFO - Iter [60650/80000] lr: 2.344e-06, eta: 1:11:20, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2169, decode.acc_seg: 91.2611, loss: 0.2169 2023-03-03 19:05:49,744 - mmseg - INFO - Iter [60700/80000] lr: 2.344e-06, eta: 1:11:09, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2094, decode.acc_seg: 91.5799, loss: 0.2094 2023-03-03 19:05:59,643 - mmseg - INFO - Iter [60750/80000] lr: 2.344e-06, eta: 1:10:57, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2146, decode.acc_seg: 91.4578, loss: 0.2146 2023-03-03 19:06:09,623 - mmseg - INFO - Iter [60800/80000] lr: 2.344e-06, eta: 1:10:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2103, decode.acc_seg: 91.6291, loss: 0.2103 2023-03-03 19:06:19,512 - mmseg - INFO - Iter [60850/80000] lr: 2.344e-06, eta: 1:10:34, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2116, decode.acc_seg: 91.5552, loss: 0.2116 2023-03-03 19:06:29,521 - mmseg - INFO - Iter [60900/80000] lr: 2.344e-06, eta: 1:10:23, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2077, decode.acc_seg: 91.8142, loss: 0.2077 2023-03-03 19:06:39,562 - mmseg - INFO - Iter [60950/80000] lr: 2.344e-06, eta: 1:10:12, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2072, decode.acc_seg: 91.7772, loss: 0.2072 2023-03-03 19:06:49,691 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:06:49,692 - mmseg - INFO - Iter [61000/80000] lr: 2.344e-06, eta: 1:10:00, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2126, decode.acc_seg: 91.4938, loss: 0.2126 2023-03-03 19:07:00,014 - mmseg - INFO - Iter [61050/80000] lr: 2.344e-06, eta: 1:09:49, time: 0.207, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2118, decode.acc_seg: 91.6143, loss: 0.2118 2023-03-03 19:07:10,394 - mmseg - INFO - Iter [61100/80000] lr: 2.344e-06, eta: 1:09:38, time: 0.208, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2195, decode.acc_seg: 91.2964, loss: 0.2195 2023-03-03 19:07:20,438 - mmseg - INFO - Iter [61150/80000] lr: 2.344e-06, eta: 1:09:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2061, decode.acc_seg: 91.6951, loss: 0.2061 2023-03-03 19:07:30,432 - mmseg - INFO - Iter [61200/80000] lr: 2.344e-06, eta: 1:09:15, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2113, decode.acc_seg: 91.5388, loss: 0.2113 2023-03-03 19:07:43,132 - mmseg - INFO - Iter [61250/80000] lr: 2.344e-06, eta: 1:09:04, time: 0.254, data_time: 0.052, memory: 67202, decode.loss_ce: 0.2049, decode.acc_seg: 91.7196, loss: 0.2049 2023-03-03 19:07:53,096 - mmseg - INFO - Iter [61300/80000] lr: 2.344e-06, eta: 1:08:53, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2171, decode.acc_seg: 91.4029, loss: 0.2171 2023-03-03 19:08:03,010 - mmseg - INFO - Iter [61350/80000] lr: 2.344e-06, eta: 1:08:42, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2071, decode.acc_seg: 91.5890, loss: 0.2071 2023-03-03 19:08:13,185 - mmseg - INFO - Iter [61400/80000] lr: 2.344e-06, eta: 1:08:30, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2033, decode.acc_seg: 91.6955, loss: 0.2033 2023-03-03 19:08:23,147 - mmseg - INFO - Iter [61450/80000] lr: 2.344e-06, eta: 1:08:19, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2180, decode.acc_seg: 91.4799, loss: 0.2180 2023-03-03 19:08:33,060 - mmseg - INFO - Iter [61500/80000] lr: 2.344e-06, eta: 1:08:08, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.6373, loss: 0.2105 2023-03-03 19:08:43,051 - mmseg - INFO - Iter [61550/80000] lr: 2.344e-06, eta: 1:07:56, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2058, decode.acc_seg: 91.6974, loss: 0.2058 2023-03-03 19:08:52,986 - mmseg - INFO - Iter [61600/80000] lr: 2.344e-06, eta: 1:07:45, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2166, decode.acc_seg: 91.3822, loss: 0.2166 2023-03-03 19:09:02,971 - mmseg - INFO - Iter [61650/80000] lr: 2.344e-06, eta: 1:07:33, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2046, decode.acc_seg: 91.7022, loss: 0.2046 2023-03-03 19:09:12,954 - mmseg - INFO - Iter [61700/80000] lr: 2.344e-06, eta: 1:07:22, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.6507, loss: 0.2090 2023-03-03 19:09:22,880 - mmseg - INFO - Iter [61750/80000] lr: 2.344e-06, eta: 1:07:11, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.6514, loss: 0.2091 2023-03-03 19:09:32,864 - mmseg - INFO - Iter [61800/80000] lr: 2.344e-06, eta: 1:06:59, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2171, decode.acc_seg: 91.3500, loss: 0.2171 2023-03-03 19:09:45,387 - mmseg - INFO - Iter [61850/80000] lr: 2.344e-06, eta: 1:06:49, time: 0.250, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.4525, loss: 0.2150 2023-03-03 19:09:55,324 - mmseg - INFO - Iter [61900/80000] lr: 2.344e-06, eta: 1:06:37, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.5112, loss: 0.2142 2023-03-03 19:10:05,196 - mmseg - INFO - Iter [61950/80000] lr: 2.344e-06, eta: 1:06:26, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.5431, loss: 0.2099 2023-03-03 19:10:15,244 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:10:15,244 - mmseg - INFO - Iter [62000/80000] lr: 2.344e-06, eta: 1:06:15, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2026, decode.acc_seg: 91.8763, loss: 0.2026 2023-03-03 19:10:25,292 - mmseg - INFO - Iter [62050/80000] lr: 2.344e-06, eta: 1:06:03, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.4734, loss: 0.2125 2023-03-03 19:10:35,261 - mmseg - INFO - Iter [62100/80000] lr: 2.344e-06, eta: 1:05:52, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2138, decode.acc_seg: 91.3716, loss: 0.2138 2023-03-03 19:10:45,538 - mmseg - INFO - Iter [62150/80000] lr: 2.344e-06, eta: 1:05:41, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.4185, loss: 0.2159 2023-03-03 19:10:55,763 - mmseg - INFO - Iter [62200/80000] lr: 2.344e-06, eta: 1:05:29, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2147, decode.acc_seg: 91.5097, loss: 0.2147 2023-03-03 19:11:05,990 - mmseg - INFO - Iter [62250/80000] lr: 2.344e-06, eta: 1:05:18, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2111, decode.acc_seg: 91.6927, loss: 0.2111 2023-03-03 19:11:16,287 - mmseg - INFO - Iter [62300/80000] lr: 2.344e-06, eta: 1:05:07, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2109, decode.acc_seg: 91.5459, loss: 0.2109 2023-03-03 19:11:26,176 - mmseg - INFO - Iter [62350/80000] lr: 2.344e-06, eta: 1:04:56, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2113, decode.acc_seg: 91.5178, loss: 0.2113 2023-03-03 19:11:36,153 - mmseg - INFO - Iter [62400/80000] lr: 2.344e-06, eta: 1:04:44, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.5432, loss: 0.2115 2023-03-03 19:11:46,030 - mmseg - INFO - Iter [62450/80000] lr: 2.344e-06, eta: 1:04:33, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.4597, loss: 0.2125 2023-03-03 19:11:58,443 - mmseg - INFO - Iter [62500/80000] lr: 2.344e-06, eta: 1:04:22, time: 0.248, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.5458, loss: 0.2142 2023-03-03 19:12:08,658 - mmseg - INFO - Iter [62550/80000] lr: 2.344e-06, eta: 1:04:11, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2118, decode.acc_seg: 91.4398, loss: 0.2118 2023-03-03 19:12:18,907 - mmseg - INFO - Iter [62600/80000] lr: 2.344e-06, eta: 1:04:00, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2041, decode.acc_seg: 91.7630, loss: 0.2041 2023-03-03 19:12:28,918 - mmseg - INFO - Iter [62650/80000] lr: 2.344e-06, eta: 1:03:48, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2155, decode.acc_seg: 91.3337, loss: 0.2155 2023-03-03 19:12:39,091 - mmseg - INFO - Iter [62700/80000] lr: 2.344e-06, eta: 1:03:37, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2126, decode.acc_seg: 91.4656, loss: 0.2126 2023-03-03 19:12:49,260 - mmseg - INFO - Iter [62750/80000] lr: 2.344e-06, eta: 1:03:26, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.7002, loss: 0.2099 2023-03-03 19:12:59,335 - mmseg - INFO - Iter [62800/80000] lr: 2.344e-06, eta: 1:03:15, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2101, decode.acc_seg: 91.5832, loss: 0.2101 2023-03-03 19:13:09,497 - mmseg - INFO - Iter [62850/80000] lr: 2.344e-06, eta: 1:03:03, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2079, decode.acc_seg: 91.7017, loss: 0.2079 2023-03-03 19:13:19,461 - mmseg - INFO - Iter [62900/80000] lr: 2.344e-06, eta: 1:02:52, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2147, decode.acc_seg: 91.3245, loss: 0.2147 2023-03-03 19:13:29,535 - mmseg - INFO - Iter [62950/80000] lr: 2.344e-06, eta: 1:02:41, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.6639, loss: 0.2080 2023-03-03 19:13:39,504 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:13:39,504 - mmseg - INFO - Iter [63000/80000] lr: 2.344e-06, eta: 1:02:29, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.4025, loss: 0.2150 2023-03-03 19:13:49,709 - mmseg - INFO - Iter [63050/80000] lr: 2.344e-06, eta: 1:02:18, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.3686, loss: 0.2127 2023-03-03 19:13:59,622 - mmseg - INFO - Iter [63100/80000] lr: 2.344e-06, eta: 1:02:07, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2130, decode.acc_seg: 91.5704, loss: 0.2130 2023-03-03 19:14:12,302 - mmseg - INFO - Iter [63150/80000] lr: 2.344e-06, eta: 1:01:56, time: 0.254, data_time: 0.051, memory: 67202, decode.loss_ce: 0.2163, decode.acc_seg: 91.4223, loss: 0.2163 2023-03-03 19:14:22,274 - mmseg - INFO - Iter [63200/80000] lr: 2.344e-06, eta: 1:01:45, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2101, decode.acc_seg: 91.5887, loss: 0.2101 2023-03-03 19:14:32,327 - mmseg - INFO - Iter [63250/80000] lr: 2.344e-06, eta: 1:01:34, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2083, decode.acc_seg: 91.6602, loss: 0.2083 2023-03-03 19:14:42,189 - mmseg - INFO - Iter [63300/80000] lr: 2.344e-06, eta: 1:01:22, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.6502, loss: 0.2081 2023-03-03 19:14:52,292 - mmseg - INFO - Iter [63350/80000] lr: 2.344e-06, eta: 1:01:11, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.5479, loss: 0.2125 2023-03-03 19:15:02,317 - mmseg - INFO - Iter [63400/80000] lr: 2.344e-06, eta: 1:01:00, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2103, decode.acc_seg: 91.5917, loss: 0.2103 2023-03-03 19:15:12,367 - mmseg - INFO - Iter [63450/80000] lr: 2.344e-06, eta: 1:00:48, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2034, decode.acc_seg: 91.7875, loss: 0.2034 2023-03-03 19:15:22,463 - mmseg - INFO - Iter [63500/80000] lr: 2.344e-06, eta: 1:00:37, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2152, decode.acc_seg: 91.5852, loss: 0.2152 2023-03-03 19:15:32,422 - mmseg - INFO - Iter [63550/80000] lr: 2.344e-06, eta: 1:00:26, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.6203, loss: 0.2099 2023-03-03 19:15:42,834 - mmseg - INFO - Iter [63600/80000] lr: 2.344e-06, eta: 1:00:15, time: 0.208, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2139, decode.acc_seg: 91.4447, loss: 0.2139 2023-03-03 19:15:53,071 - mmseg - INFO - Iter [63650/80000] lr: 2.344e-06, eta: 1:00:03, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.6048, loss: 0.2060 2023-03-03 19:16:03,143 - mmseg - INFO - Iter [63700/80000] lr: 2.344e-06, eta: 0:59:52, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2171, decode.acc_seg: 91.3173, loss: 0.2171 2023-03-03 19:16:15,511 - mmseg - INFO - Iter [63750/80000] lr: 2.344e-06, eta: 0:59:41, time: 0.247, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.5010, loss: 0.2125 2023-03-03 19:16:25,470 - mmseg - INFO - Iter [63800/80000] lr: 2.344e-06, eta: 0:59:30, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.5747, loss: 0.2108 2023-03-03 19:16:35,517 - mmseg - INFO - Iter [63850/80000] lr: 2.344e-06, eta: 0:59:19, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2167, decode.acc_seg: 91.4704, loss: 0.2167 2023-03-03 19:16:45,479 - mmseg - INFO - Iter [63900/80000] lr: 2.344e-06, eta: 0:59:08, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2048, decode.acc_seg: 91.8471, loss: 0.2048 2023-03-03 19:16:55,334 - mmseg - INFO - Iter [63950/80000] lr: 2.344e-06, eta: 0:58:56, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.4850, loss: 0.2159 2023-03-03 19:17:05,521 - mmseg - INFO - Saving checkpoint at 64000 iterations 2023-03-03 19:17:06,399 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:17:06,399 - mmseg - INFO - Iter [64000/80000] lr: 2.344e-06, eta: 0:58:45, time: 0.221, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2130, decode.acc_seg: 91.4176, loss: 0.2130 2023-03-03 19:17:20,934 - mmseg - INFO - per class results: 2023-03-03 19:17:20,941 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 75.88 | 88.37 | | building | 82.64 | 93.24 | | sky | 94.12 | 97.3 | | floor | 78.9 | 90.36 | | tree | 73.12 | 87.75 | | ceiling | 82.32 | 91.11 | | road | 80.44 | 88.15 | | bed | 87.82 | 95.07 | | windowpane | 59.66 | 76.19 | | grass | 65.61 | 83.73 | | cabinet | 58.73 | 73.14 | | sidewalk | 63.81 | 80.8 | | person | 77.79 | 91.18 | | earth | 31.9 | 44.24 | | door | 45.78 | 60.38 | | table | 59.95 | 76.14 | | mountain | 51.32 | 68.53 | | plant | 51.01 | 64.18 | | curtain | 71.35 | 82.9 | | chair | 54.73 | 69.49 | | car | 81.51 | 90.05 | | water | 45.41 | 60.19 | | painting | 71.53 | 85.34 | | sofa | 63.13 | 82.06 | | shelf | 39.24 | 56.53 | | house | 47.25 | 56.54 | | sea | 43.17 | 70.36 | | mirror | 63.54 | 71.8 | | rug | 56.37 | 63.09 | | field | 22.74 | 34.81 | | armchair | 41.35 | 58.14 | | seat | 58.22 | 76.37 | | fence | 34.33 | 44.86 | | desk | 48.85 | 68.11 | | rock | 29.07 | 46.08 | | wardrobe | 45.39 | 59.53 | | lamp | 61.97 | 75.82 | | bathtub | 74.9 | 80.8 | | railing | 28.62 | 41.62 | | cushion | 53.01 | 65.2 | | base | 20.56 | 31.45 | | box | 20.93 | 26.36 | | column | 44.68 | 57.08 | | signboard | 36.15 | 49.12 | | chest of drawers | 37.59 | 55.86 | | counter | 28.16 | 35.03 | | sand | 30.51 | 48.83 | | sink | 66.47 | 78.57 | | skyscraper | 69.65 | 79.38 | | fireplace | 70.74 | 85.99 | | refrigerator | 70.74 | 83.11 | | grandstand | 39.73 | 61.58 | | path | 15.71 | 23.27 | | stairs | 30.06 | 36.91 | | runway | 60.0 | 79.24 | | case | 44.15 | 66.4 | | pool table | 91.51 | 95.67 | | pillow | 54.05 | 66.82 | | screen door | 67.16 | 73.77 | | stairway | 30.56 | 40.55 | | river | 12.11 | 22.84 | | bridge | 63.97 | 70.2 | | bookcase | 38.86 | 48.27 | | blind | 40.18 | 46.67 | | coffee table | 58.21 | 76.62 | | toilet | 85.36 | 91.12 | | flower | 34.76 | 45.44 | | book | 45.64 | 63.32 | | hill | 4.43 | 6.06 | | bench | 37.51 | 48.49 | | countertop | 56.01 | 72.57 | | stove | 72.32 | 81.87 | | palm | 51.74 | 72.44 | | kitchen island | 45.81 | 76.64 | | computer | 55.25 | 64.52 | | swivel chair | 44.75 | 61.58 | | boat | 46.89 | 57.92 | | bar | 25.1 | 30.16 | | arcade machine | 23.24 | 25.24 | | hovel | 37.12 | 39.09 | | bus | 78.91 | 87.47 | | towel | 56.19 | 66.44 | | light | 53.82 | 60.81 | | truck | 31.45 | 41.06 | | tower | 34.95 | 42.74 | | chandelier | 67.85 | 82.46 | | awning | 26.01 | 29.56 | | streetlight | 25.87 | 31.73 | | booth | 39.96 | 44.01 | | television receiver | 67.39 | 77.87 | | airplane | 49.46 | 62.22 | | dirt track | 2.12 | 4.73 | | apparel | 29.48 | 40.9 | | pole | 23.79 | 35.28 | | land | 0.73 | 1.04 | | bannister | 11.06 | 13.96 | | escalator | 21.38 | 22.4 | | ottoman | 44.27 | 57.64 | | bottle | 13.18 | 21.87 | | buffet | 34.37 | 42.97 | | poster | 26.57 | 34.59 | | stage | 8.52 | 10.97 | | van | 41.33 | 58.08 | | ship | 67.83 | 78.3 | | fountain | 0.25 | 0.25 | | conveyer belt | 61.59 | 85.4 | | canopy | 15.57 | 17.97 | | washer | 63.58 | 65.27 | | plaything | 21.18 | 26.01 | | swimming pool | 29.32 | 35.64 | | stool | 41.35 | 53.69 | | barrel | 38.64 | 64.6 | | basket | 21.64 | 33.77 | | waterfall | 58.76 | 77.24 | | tent | 93.21 | 98.36 | | bag | 9.36 | 11.38 | | minibike | 52.04 | 59.89 | | cradle | 75.76 | 96.73 | | oven | 21.81 | 51.39 | | ball | 45.74 | 67.09 | | food | 50.38 | 58.66 | | step | 2.95 | 3.44 | | tank | 45.54 | 46.1 | | trade name | 20.76 | 23.66 | | microwave | 38.52 | 41.29 | | pot | 36.42 | 43.0 | | animal | 49.18 | 50.57 | | bicycle | 46.35 | 70.61 | | lake | 60.47 | 63.0 | | dishwasher | 72.3 | 76.42 | | screen | 58.86 | 69.57 | | blanket | 7.21 | 8.46 | | sculpture | 39.91 | 66.39 | | hood | 61.19 | 69.07 | | sconce | 40.92 | 47.7 | | vase | 31.73 | 49.4 | | traffic light | 27.28 | 39.54 | | tray | 4.31 | 6.25 | | ashcan | 41.02 | 54.77 | | fan | 55.82 | 68.38 | | pier | 23.1 | 30.74 | | crt screen | 4.47 | 7.97 | | plate | 39.47 | 50.6 | | monitor | 64.13 | 75.03 | | bulletin board | 35.49 | 50.37 | | shower | 1.2 | 1.75 | | radiator | 41.48 | 48.76 | | glass | 9.95 | 11.06 | | clock | 18.97 | 23.77 | | flag | 39.68 | 42.94 | +---------------------+-------+-------+ 2023-03-03 19:17:20,941 - mmseg - INFO - Summary: 2023-03-03 19:17:20,941 - mmseg - INFO - +------+-------+-------+ | aAcc | mIoU | mAcc | +------+-------+-------+ | 81.6 | 44.93 | 55.72 | +------+-------+-------+ 2023-03-03 19:17:20,970 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_56000.pth was removed 2023-03-03 19:17:21,876 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_64000.pth. 2023-03-03 19:17:21,877 - mmseg - INFO - Best mIoU is 0.4493 at 64000 iter. 2023-03-03 19:17:21,878 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:17:21,878 - mmseg - INFO - Iter(val) [250] aAcc: 0.8160, mIoU: 0.4493, mAcc: 0.5572, IoU.background: nan, IoU.wall: 0.7588, IoU.building: 0.8264, IoU.sky: 0.9412, IoU.floor: 0.7890, IoU.tree: 0.7312, IoU.ceiling: 0.8232, IoU.road: 0.8044, IoU.bed : 0.8782, IoU.windowpane: 0.5966, IoU.grass: 0.6561, IoU.cabinet: 0.5873, IoU.sidewalk: 0.6381, IoU.person: 0.7779, IoU.earth: 0.3190, IoU.door: 0.4578, IoU.table: 0.5995, IoU.mountain: 0.5132, IoU.plant: 0.5101, IoU.curtain: 0.7135, IoU.chair: 0.5473, IoU.car: 0.8151, IoU.water: 0.4541, IoU.painting: 0.7153, IoU.sofa: 0.6313, IoU.shelf: 0.3924, IoU.house: 0.4725, IoU.sea: 0.4317, IoU.mirror: 0.6354, IoU.rug: 0.5637, IoU.field: 0.2274, IoU.armchair: 0.4135, IoU.seat: 0.5822, IoU.fence: 0.3433, IoU.desk: 0.4885, IoU.rock: 0.2907, IoU.wardrobe: 0.4539, IoU.lamp: 0.6197, IoU.bathtub: 0.7490, IoU.railing: 0.2862, IoU.cushion: 0.5301, IoU.base: 0.2056, IoU.box: 0.2093, IoU.column: 0.4468, IoU.signboard: 0.3615, IoU.chest of drawers: 0.3759, IoU.counter: 0.2816, IoU.sand: 0.3051, IoU.sink: 0.6647, IoU.skyscraper: 0.6965, IoU.fireplace: 0.7074, IoU.refrigerator: 0.7074, IoU.grandstand: 0.3973, IoU.path: 0.1571, IoU.stairs: 0.3006, IoU.runway: 0.6000, IoU.case: 0.4415, IoU.pool table: 0.9151, IoU.pillow: 0.5405, IoU.screen door: 0.6716, IoU.stairway: 0.3056, IoU.river: 0.1211, IoU.bridge: 0.6397, IoU.bookcase: 0.3886, IoU.blind: 0.4018, IoU.coffee table: 0.5821, IoU.toilet: 0.8536, IoU.flower: 0.3476, IoU.book: 0.4564, IoU.hill: 0.0443, IoU.bench: 0.3751, IoU.countertop: 0.5601, IoU.stove: 0.7232, IoU.palm: 0.5174, IoU.kitchen island: 0.4581, IoU.computer: 0.5525, IoU.swivel chair: 0.4475, IoU.boat: 0.4689, IoU.bar: 0.2510, IoU.arcade machine: 0.2324, IoU.hovel: 0.3712, IoU.bus: 0.7891, IoU.towel: 0.5619, IoU.light: 0.5382, IoU.truck: 0.3145, IoU.tower: 0.3495, IoU.chandelier: 0.6785, IoU.awning: 0.2601, IoU.streetlight: 0.2587, IoU.booth: 0.3996, IoU.television receiver: 0.6739, IoU.airplane: 0.4946, IoU.dirt track: 0.0212, IoU.apparel: 0.2948, IoU.pole: 0.2379, IoU.land: 0.0073, IoU.bannister: 0.1106, IoU.escalator: 0.2138, IoU.ottoman: 0.4427, IoU.bottle: 0.1318, IoU.buffet: 0.3437, IoU.poster: 0.2657, IoU.stage: 0.0852, IoU.van: 0.4133, IoU.ship: 0.6783, IoU.fountain: 0.0025, IoU.conveyer belt: 0.6159, IoU.canopy: 0.1557, IoU.washer: 0.6358, IoU.plaything: 0.2118, IoU.swimming pool: 0.2932, IoU.stool: 0.4135, IoU.barrel: 0.3864, IoU.basket: 0.2164, IoU.waterfall: 0.5876, IoU.tent: 0.9321, IoU.bag: 0.0936, IoU.minibike: 0.5204, IoU.cradle: 0.7576, IoU.oven: 0.2181, IoU.ball: 0.4574, IoU.food: 0.5038, IoU.step: 0.0295, IoU.tank: 0.4554, IoU.trade name: 0.2076, IoU.microwave: 0.3852, IoU.pot: 0.3642, IoU.animal: 0.4918, IoU.bicycle: 0.4635, IoU.lake: 0.6047, IoU.dishwasher: 0.7230, IoU.screen: 0.5886, IoU.blanket: 0.0721, IoU.sculpture: 0.3991, IoU.hood: 0.6119, IoU.sconce: 0.4092, IoU.vase: 0.3173, IoU.traffic light: 0.2728, IoU.tray: 0.0431, IoU.ashcan: 0.4102, IoU.fan: 0.5582, IoU.pier: 0.2310, IoU.crt screen: 0.0447, IoU.plate: 0.3947, IoU.monitor: 0.6413, IoU.bulletin board: 0.3549, IoU.shower: 0.0120, IoU.radiator: 0.4148, IoU.glass: 0.0995, IoU.clock: 0.1897, IoU.flag: 0.3968, Acc.background: nan, Acc.wall: 0.8837, Acc.building: 0.9324, Acc.sky: 0.9730, Acc.floor: 0.9036, Acc.tree: 0.8775, Acc.ceiling: 0.9111, Acc.road: 0.8815, Acc.bed : 0.9507, Acc.windowpane: 0.7619, Acc.grass: 0.8373, Acc.cabinet: 0.7314, Acc.sidewalk: 0.8080, Acc.person: 0.9118, Acc.earth: 0.4424, Acc.door: 0.6038, Acc.table: 0.7614, Acc.mountain: 0.6853, Acc.plant: 0.6418, Acc.curtain: 0.8290, Acc.chair: 0.6949, Acc.car: 0.9005, Acc.water: 0.6019, Acc.painting: 0.8534, Acc.sofa: 0.8206, Acc.shelf: 0.5653, Acc.house: 0.5654, Acc.sea: 0.7036, Acc.mirror: 0.7180, Acc.rug: 0.6309, Acc.field: 0.3481, Acc.armchair: 0.5814, Acc.seat: 0.7637, Acc.fence: 0.4486, Acc.desk: 0.6811, Acc.rock: 0.4608, Acc.wardrobe: 0.5953, Acc.lamp: 0.7582, Acc.bathtub: 0.8080, Acc.railing: 0.4162, Acc.cushion: 0.6520, Acc.base: 0.3145, Acc.box: 0.2636, Acc.column: 0.5708, Acc.signboard: 0.4912, Acc.chest of drawers: 0.5586, Acc.counter: 0.3503, Acc.sand: 0.4883, Acc.sink: 0.7857, Acc.skyscraper: 0.7938, Acc.fireplace: 0.8599, Acc.refrigerator: 0.8311, Acc.grandstand: 0.6158, Acc.path: 0.2327, Acc.stairs: 0.3691, Acc.runway: 0.7924, Acc.case: 0.6640, Acc.pool table: 0.9567, Acc.pillow: 0.6682, Acc.screen door: 0.7377, Acc.stairway: 0.4055, Acc.river: 0.2284, Acc.bridge: 0.7020, Acc.bookcase: 0.4827, Acc.blind: 0.4667, Acc.coffee table: 0.7662, Acc.toilet: 0.9112, Acc.flower: 0.4544, Acc.book: 0.6332, Acc.hill: 0.0606, Acc.bench: 0.4849, Acc.countertop: 0.7257, Acc.stove: 0.8187, Acc.palm: 0.7244, Acc.kitchen island: 0.7664, Acc.computer: 0.6452, Acc.swivel chair: 0.6158, Acc.boat: 0.5792, Acc.bar: 0.3016, Acc.arcade machine: 0.2524, Acc.hovel: 0.3909, Acc.bus: 0.8747, Acc.towel: 0.6644, Acc.light: 0.6081, Acc.truck: 0.4106, Acc.tower: 0.4274, Acc.chandelier: 0.8246, Acc.awning: 0.2956, Acc.streetlight: 0.3173, Acc.booth: 0.4401, Acc.television receiver: 0.7787, Acc.airplane: 0.6222, Acc.dirt track: 0.0473, Acc.apparel: 0.4090, Acc.pole: 0.3528, Acc.land: 0.0104, Acc.bannister: 0.1396, Acc.escalator: 0.2240, Acc.ottoman: 0.5764, Acc.bottle: 0.2187, Acc.buffet: 0.4297, Acc.poster: 0.3459, Acc.stage: 0.1097, Acc.van: 0.5808, Acc.ship: 0.7830, Acc.fountain: 0.0025, Acc.conveyer belt: 0.8540, Acc.canopy: 0.1797, Acc.washer: 0.6527, Acc.plaything: 0.2601, Acc.swimming pool: 0.3564, Acc.stool: 0.5369, Acc.barrel: 0.6460, Acc.basket: 0.3377, Acc.waterfall: 0.7724, Acc.tent: 0.9836, Acc.bag: 0.1138, Acc.minibike: 0.5989, Acc.cradle: 0.9673, Acc.oven: 0.5139, Acc.ball: 0.6709, Acc.food: 0.5866, Acc.step: 0.0344, Acc.tank: 0.4610, Acc.trade name: 0.2366, Acc.microwave: 0.4129, Acc.pot: 0.4300, Acc.animal: 0.5057, Acc.bicycle: 0.7061, Acc.lake: 0.6300, Acc.dishwasher: 0.7642, Acc.screen: 0.6957, Acc.blanket: 0.0846, Acc.sculpture: 0.6639, Acc.hood: 0.6907, Acc.sconce: 0.4770, Acc.vase: 0.4940, Acc.traffic light: 0.3954, Acc.tray: 0.0625, Acc.ashcan: 0.5477, Acc.fan: 0.6838, Acc.pier: 0.3074, Acc.crt screen: 0.0797, Acc.plate: 0.5060, Acc.monitor: 0.7503, Acc.bulletin board: 0.5037, Acc.shower: 0.0175, Acc.radiator: 0.4876, Acc.glass: 0.1106, Acc.clock: 0.2377, Acc.flag: 0.4294 2023-03-03 19:17:32,342 - mmseg - INFO - Iter [64050/80000] lr: 2.344e-06, eta: 0:58:38, time: 0.519, data_time: 0.317, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.7235, loss: 0.2060 2023-03-03 19:17:42,483 - mmseg - INFO - Iter [64100/80000] lr: 2.344e-06, eta: 0:58:27, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2012, decode.acc_seg: 91.9465, loss: 0.2012 2023-03-03 19:17:52,549 - mmseg - INFO - Iter [64150/80000] lr: 2.344e-06, eta: 0:58:16, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.4855, loss: 0.2141 2023-03-03 19:18:02,680 - mmseg - INFO - Iter [64200/80000] lr: 2.344e-06, eta: 0:58:04, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2113, decode.acc_seg: 91.2788, loss: 0.2113 2023-03-03 19:18:12,556 - mmseg - INFO - Iter [64250/80000] lr: 2.344e-06, eta: 0:57:53, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2148, decode.acc_seg: 91.4418, loss: 0.2148 2023-03-03 19:18:22,445 - mmseg - INFO - Iter [64300/80000] lr: 2.344e-06, eta: 0:57:42, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2119, decode.acc_seg: 91.4399, loss: 0.2119 2023-03-03 19:18:32,324 - mmseg - INFO - Iter [64350/80000] lr: 2.344e-06, eta: 0:57:30, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2067, decode.acc_seg: 91.7512, loss: 0.2067 2023-03-03 19:18:44,769 - mmseg - INFO - Iter [64400/80000] lr: 2.344e-06, eta: 0:57:20, time: 0.249, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2083, decode.acc_seg: 91.7117, loss: 0.2083 2023-03-03 19:18:54,775 - mmseg - INFO - Iter [64450/80000] lr: 2.344e-06, eta: 0:57:08, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2107, decode.acc_seg: 91.6082, loss: 0.2107 2023-03-03 19:19:04,934 - mmseg - INFO - Iter [64500/80000] lr: 2.344e-06, eta: 0:56:57, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.6361, loss: 0.2128 2023-03-03 19:19:14,868 - mmseg - INFO - Iter [64550/80000] lr: 2.344e-06, eta: 0:56:46, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2170, decode.acc_seg: 91.3332, loss: 0.2170 2023-03-03 19:19:24,929 - mmseg - INFO - Iter [64600/80000] lr: 2.344e-06, eta: 0:56:35, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2030, decode.acc_seg: 91.7972, loss: 0.2030 2023-03-03 19:19:34,897 - mmseg - INFO - Iter [64650/80000] lr: 2.344e-06, eta: 0:56:23, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2016, decode.acc_seg: 91.8095, loss: 0.2016 2023-03-03 19:19:45,079 - mmseg - INFO - Iter [64700/80000] lr: 2.344e-06, eta: 0:56:12, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2063, decode.acc_seg: 91.6631, loss: 0.2063 2023-03-03 19:19:55,188 - mmseg - INFO - Iter [64750/80000] lr: 2.344e-06, eta: 0:56:01, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2073, decode.acc_seg: 91.7600, loss: 0.2073 2023-03-03 19:20:05,444 - mmseg - INFO - Iter [64800/80000] lr: 2.344e-06, eta: 0:55:50, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2044, decode.acc_seg: 91.8681, loss: 0.2044 2023-03-03 19:20:15,365 - mmseg - INFO - Iter [64850/80000] lr: 2.344e-06, eta: 0:55:38, time: 0.198, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.3844, loss: 0.2117 2023-03-03 19:20:25,330 - mmseg - INFO - Iter [64900/80000] lr: 2.344e-06, eta: 0:55:27, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2095, decode.acc_seg: 91.5568, loss: 0.2095 2023-03-03 19:20:35,252 - mmseg - INFO - Iter [64950/80000] lr: 2.344e-06, eta: 0:55:16, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2074, decode.acc_seg: 91.6696, loss: 0.2074 2023-03-03 19:20:47,749 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:20:47,749 - mmseg - INFO - Iter [65000/80000] lr: 2.344e-06, eta: 0:55:05, time: 0.250, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2147, decode.acc_seg: 91.3109, loss: 0.2147 2023-03-03 19:20:57,849 - mmseg - INFO - Iter [65050/80000] lr: 2.344e-06, eta: 0:54:54, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2100, decode.acc_seg: 91.4809, loss: 0.2100 2023-03-03 19:21:08,113 - mmseg - INFO - Iter [65100/80000] lr: 2.344e-06, eta: 0:54:43, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2032, decode.acc_seg: 91.8269, loss: 0.2032 2023-03-03 19:21:18,135 - mmseg - INFO - Iter [65150/80000] lr: 2.344e-06, eta: 0:54:32, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.3505, loss: 0.2141 2023-03-03 19:21:28,085 - mmseg - INFO - Iter [65200/80000] lr: 2.344e-06, eta: 0:54:20, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2078, decode.acc_seg: 91.7259, loss: 0.2078 2023-03-03 19:21:38,047 - mmseg - INFO - Iter [65250/80000] lr: 2.344e-06, eta: 0:54:09, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.6549, loss: 0.2122 2023-03-03 19:21:48,005 - mmseg - INFO - Iter [65300/80000] lr: 2.344e-06, eta: 0:53:58, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.4330, loss: 0.2151 2023-03-03 19:21:57,866 - mmseg - INFO - Iter [65350/80000] lr: 2.344e-06, eta: 0:53:46, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2047, decode.acc_seg: 91.8358, loss: 0.2047 2023-03-03 19:22:07,872 - mmseg - INFO - Iter [65400/80000] lr: 2.344e-06, eta: 0:53:35, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.4324, loss: 0.2124 2023-03-03 19:22:17,914 - mmseg - INFO - Iter [65450/80000] lr: 2.344e-06, eta: 0:53:24, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2008, decode.acc_seg: 91.8925, loss: 0.2008 2023-03-03 19:22:27,798 - mmseg - INFO - Iter [65500/80000] lr: 2.344e-06, eta: 0:53:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.5834, loss: 0.2099 2023-03-03 19:22:37,856 - mmseg - INFO - Iter [65550/80000] lr: 2.344e-06, eta: 0:53:02, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2027, decode.acc_seg: 91.9490, loss: 0.2027 2023-03-03 19:22:47,802 - mmseg - INFO - Iter [65600/80000] lr: 2.344e-06, eta: 0:52:50, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2123, decode.acc_seg: 91.4942, loss: 0.2123 2023-03-03 19:23:00,340 - mmseg - INFO - Iter [65650/80000] lr: 2.344e-06, eta: 0:52:40, time: 0.251, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.5347, loss: 0.2125 2023-03-03 19:23:10,301 - mmseg - INFO - Iter [65700/80000] lr: 2.344e-06, eta: 0:52:28, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2196, decode.acc_seg: 91.1944, loss: 0.2196 2023-03-03 19:23:20,569 - mmseg - INFO - Iter [65750/80000] lr: 2.344e-06, eta: 0:52:17, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2076, decode.acc_seg: 91.6006, loss: 0.2076 2023-03-03 19:23:30,750 - mmseg - INFO - Iter [65800/80000] lr: 2.344e-06, eta: 0:52:06, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.5313, loss: 0.2122 2023-03-03 19:23:40,770 - mmseg - INFO - Iter [65850/80000] lr: 2.344e-06, eta: 0:51:55, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2067, decode.acc_seg: 91.6725, loss: 0.2067 2023-03-03 19:23:50,748 - mmseg - INFO - Iter [65900/80000] lr: 2.344e-06, eta: 0:51:44, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2098, decode.acc_seg: 91.5805, loss: 0.2098 2023-03-03 19:24:00,610 - mmseg - INFO - Iter [65950/80000] lr: 2.344e-06, eta: 0:51:32, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1981, decode.acc_seg: 92.1783, loss: 0.1981 2023-03-03 19:24:10,880 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:24:10,880 - mmseg - INFO - Iter [66000/80000] lr: 2.344e-06, eta: 0:51:21, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2046, decode.acc_seg: 91.8476, loss: 0.2046 2023-03-03 19:24:21,076 - mmseg - INFO - Iter [66050/80000] lr: 2.344e-06, eta: 0:51:10, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2123, decode.acc_seg: 91.5048, loss: 0.2123 2023-03-03 19:24:31,258 - mmseg - INFO - Iter [66100/80000] lr: 2.344e-06, eta: 0:50:59, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.4680, loss: 0.2159 2023-03-03 19:24:41,259 - mmseg - INFO - Iter [66150/80000] lr: 2.344e-06, eta: 0:50:48, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.4608, loss: 0.2149 2023-03-03 19:24:51,222 - mmseg - INFO - Iter [66200/80000] lr: 2.344e-06, eta: 0:50:36, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2003, decode.acc_seg: 91.8618, loss: 0.2003 2023-03-03 19:25:01,310 - mmseg - INFO - Iter [66250/80000] lr: 2.344e-06, eta: 0:50:25, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2040, decode.acc_seg: 91.6706, loss: 0.2040 2023-03-03 19:25:14,119 - mmseg - INFO - Iter [66300/80000] lr: 2.344e-06, eta: 0:50:15, time: 0.256, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.6819, loss: 0.2105 2023-03-03 19:25:24,128 - mmseg - INFO - Iter [66350/80000] lr: 2.344e-06, eta: 0:50:03, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.5188, loss: 0.2127 2023-03-03 19:25:34,094 - mmseg - INFO - Iter [66400/80000] lr: 2.344e-06, eta: 0:49:52, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2076, decode.acc_seg: 91.8112, loss: 0.2076 2023-03-03 19:25:44,041 - mmseg - INFO - Iter [66450/80000] lr: 2.344e-06, eta: 0:49:41, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.5460, loss: 0.2144 2023-03-03 19:25:53,976 - mmseg - INFO - Iter [66500/80000] lr: 2.344e-06, eta: 0:49:30, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2041, decode.acc_seg: 91.8292, loss: 0.2041 2023-03-03 19:26:03,862 - mmseg - INFO - Iter [66550/80000] lr: 2.344e-06, eta: 0:49:18, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.5641, loss: 0.2145 2023-03-03 19:26:13,976 - mmseg - INFO - Iter [66600/80000] lr: 2.344e-06, eta: 0:49:07, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2022, decode.acc_seg: 91.7524, loss: 0.2022 2023-03-03 19:26:24,075 - mmseg - INFO - Iter [66650/80000] lr: 2.344e-06, eta: 0:48:56, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2039, decode.acc_seg: 91.9194, loss: 0.2039 2023-03-03 19:26:34,045 - mmseg - INFO - Iter [66700/80000] lr: 2.344e-06, eta: 0:48:45, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2154, decode.acc_seg: 91.4199, loss: 0.2154 2023-03-03 19:26:44,176 - mmseg - INFO - Iter [66750/80000] lr: 2.344e-06, eta: 0:48:34, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2087, decode.acc_seg: 91.6174, loss: 0.2087 2023-03-03 19:26:54,203 - mmseg - INFO - Iter [66800/80000] lr: 2.344e-06, eta: 0:48:23, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.5543, loss: 0.2144 2023-03-03 19:27:04,267 - mmseg - INFO - Iter [66850/80000] lr: 2.344e-06, eta: 0:48:11, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2096, decode.acc_seg: 91.5827, loss: 0.2096 2023-03-03 19:27:16,792 - mmseg - INFO - Iter [66900/80000] lr: 2.344e-06, eta: 0:48:01, time: 0.250, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2049, decode.acc_seg: 91.7872, loss: 0.2049 2023-03-03 19:27:26,735 - mmseg - INFO - Iter [66950/80000] lr: 2.344e-06, eta: 0:47:49, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2036, decode.acc_seg: 91.7654, loss: 0.2036 2023-03-03 19:27:36,721 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:27:36,721 - mmseg - INFO - Iter [67000/80000] lr: 2.344e-06, eta: 0:47:38, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2138, decode.acc_seg: 91.3649, loss: 0.2138 2023-03-03 19:27:46,733 - mmseg - INFO - Iter [67050/80000] lr: 2.344e-06, eta: 0:47:27, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.4290, loss: 0.2135 2023-03-03 19:27:56,745 - mmseg - INFO - Iter [67100/80000] lr: 2.344e-06, eta: 0:47:16, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.5211, loss: 0.2108 2023-03-03 19:28:06,763 - mmseg - INFO - Iter [67150/80000] lr: 2.344e-06, eta: 0:47:05, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.6050, loss: 0.2110 2023-03-03 19:28:16,633 - mmseg - INFO - Iter [67200/80000] lr: 2.344e-06, eta: 0:46:54, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2091, decode.acc_seg: 91.4630, loss: 0.2091 2023-03-03 19:28:26,690 - mmseg - INFO - Iter [67250/80000] lr: 2.344e-06, eta: 0:46:42, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.6100, loss: 0.2086 2023-03-03 19:28:36,595 - mmseg - INFO - Iter [67300/80000] lr: 2.344e-06, eta: 0:46:31, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.6800, loss: 0.2090 2023-03-03 19:28:46,568 - mmseg - INFO - Iter [67350/80000] lr: 2.344e-06, eta: 0:46:20, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2082, decode.acc_seg: 91.6635, loss: 0.2082 2023-03-03 19:28:56,516 - mmseg - INFO - Iter [67400/80000] lr: 2.344e-06, eta: 0:46:09, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2138, decode.acc_seg: 91.3891, loss: 0.2138 2023-03-03 19:29:06,779 - mmseg - INFO - Iter [67450/80000] lr: 2.344e-06, eta: 0:45:58, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2088, decode.acc_seg: 91.5751, loss: 0.2088 2023-03-03 19:29:17,143 - mmseg - INFO - Iter [67500/80000] lr: 2.344e-06, eta: 0:45:47, time: 0.207, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2066, decode.acc_seg: 91.8103, loss: 0.2066 2023-03-03 19:29:29,602 - mmseg - INFO - Iter [67550/80000] lr: 2.344e-06, eta: 0:45:36, time: 0.249, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2061, decode.acc_seg: 91.7751, loss: 0.2061 2023-03-03 19:29:39,706 - mmseg - INFO - Iter [67600/80000] lr: 2.344e-06, eta: 0:45:25, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2066, decode.acc_seg: 91.7926, loss: 0.2066 2023-03-03 19:29:49,784 - mmseg - INFO - Iter [67650/80000] lr: 2.344e-06, eta: 0:45:14, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2011, decode.acc_seg: 91.9329, loss: 0.2011 2023-03-03 19:29:59,759 - mmseg - INFO - Iter [67700/80000] lr: 2.344e-06, eta: 0:45:02, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2066, decode.acc_seg: 91.8879, loss: 0.2066 2023-03-03 19:30:09,700 - mmseg - INFO - Iter [67750/80000] lr: 2.344e-06, eta: 0:44:51, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2078, decode.acc_seg: 91.6067, loss: 0.2078 2023-03-03 19:30:19,659 - mmseg - INFO - Iter [67800/80000] lr: 2.344e-06, eta: 0:44:40, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2124, decode.acc_seg: 91.4642, loss: 0.2124 2023-03-03 19:30:29,551 - mmseg - INFO - Iter [67850/80000] lr: 2.344e-06, eta: 0:44:29, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.3349, loss: 0.2153 2023-03-03 19:30:39,514 - mmseg - INFO - Iter [67900/80000] lr: 2.344e-06, eta: 0:44:18, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2131, decode.acc_seg: 91.6585, loss: 0.2131 2023-03-03 19:30:49,670 - mmseg - INFO - Iter [67950/80000] lr: 2.344e-06, eta: 0:44:07, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.5294, loss: 0.2142 2023-03-03 19:30:59,588 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:30:59,588 - mmseg - INFO - Iter [68000/80000] lr: 2.344e-06, eta: 0:43:55, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.3618, loss: 0.2135 2023-03-03 19:31:09,684 - mmseg - INFO - Iter [68050/80000] lr: 2.344e-06, eta: 0:43:44, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.6942, loss: 0.2086 2023-03-03 19:31:19,570 - mmseg - INFO - Iter [68100/80000] lr: 2.344e-06, eta: 0:43:33, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2150, decode.acc_seg: 91.3394, loss: 0.2150 2023-03-03 19:31:31,959 - mmseg - INFO - Iter [68150/80000] lr: 2.344e-06, eta: 0:43:22, time: 0.248, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2139, decode.acc_seg: 91.4491, loss: 0.2139 2023-03-03 19:31:41,960 - mmseg - INFO - Iter [68200/80000] lr: 2.344e-06, eta: 0:43:11, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2048, decode.acc_seg: 91.9075, loss: 0.2048 2023-03-03 19:31:52,143 - mmseg - INFO - Iter [68250/80000] lr: 2.344e-06, eta: 0:43:00, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2101, decode.acc_seg: 91.6130, loss: 0.2101 2023-03-03 19:32:02,030 - mmseg - INFO - Iter [68300/80000] lr: 2.344e-06, eta: 0:42:49, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2071, decode.acc_seg: 91.7473, loss: 0.2071 2023-03-03 19:32:12,174 - mmseg - INFO - Iter [68350/80000] lr: 2.344e-06, eta: 0:42:38, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.4699, loss: 0.2117 2023-03-03 19:32:22,077 - mmseg - INFO - Iter [68400/80000] lr: 2.344e-06, eta: 0:42:27, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2059, decode.acc_seg: 91.7575, loss: 0.2059 2023-03-03 19:32:32,004 - mmseg - INFO - Iter [68450/80000] lr: 2.344e-06, eta: 0:42:15, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2167, decode.acc_seg: 91.3675, loss: 0.2167 2023-03-03 19:32:42,189 - mmseg - INFO - Iter [68500/80000] lr: 2.344e-06, eta: 0:42:04, time: 0.204, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2114, decode.acc_seg: 91.6944, loss: 0.2114 2023-03-03 19:32:52,243 - mmseg - INFO - Iter [68550/80000] lr: 2.344e-06, eta: 0:41:53, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2082, decode.acc_seg: 91.5153, loss: 0.2082 2023-03-03 19:33:02,248 - mmseg - INFO - Iter [68600/80000] lr: 2.344e-06, eta: 0:41:42, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2109, decode.acc_seg: 91.4350, loss: 0.2109 2023-03-03 19:33:12,274 - mmseg - INFO - Iter [68650/80000] lr: 2.344e-06, eta: 0:41:31, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2073, decode.acc_seg: 91.6771, loss: 0.2073 2023-03-03 19:33:22,335 - mmseg - INFO - Iter [68700/80000] lr: 2.344e-06, eta: 0:41:20, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.3401, loss: 0.2127 2023-03-03 19:33:32,275 - mmseg - INFO - Iter [68750/80000] lr: 2.344e-06, eta: 0:41:09, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2085, decode.acc_seg: 91.6857, loss: 0.2085 2023-03-03 19:33:44,856 - mmseg - INFO - Iter [68800/80000] lr: 2.344e-06, eta: 0:40:58, time: 0.252, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.4941, loss: 0.2117 2023-03-03 19:33:54,798 - mmseg - INFO - Iter [68850/80000] lr: 2.344e-06, eta: 0:40:47, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2051, decode.acc_seg: 91.6781, loss: 0.2051 2023-03-03 19:34:04,921 - mmseg - INFO - Iter [68900/80000] lr: 2.344e-06, eta: 0:40:36, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.6744, loss: 0.2060 2023-03-03 19:34:14,953 - mmseg - INFO - Iter [68950/80000] lr: 2.344e-06, eta: 0:40:25, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2106, decode.acc_seg: 91.5642, loss: 0.2106 2023-03-03 19:34:24,873 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:34:24,873 - mmseg - INFO - Iter [69000/80000] lr: 2.344e-06, eta: 0:40:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2172, decode.acc_seg: 91.4859, loss: 0.2172 2023-03-03 19:34:34,909 - mmseg - INFO - Iter [69050/80000] lr: 2.344e-06, eta: 0:40:02, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.4835, loss: 0.2144 2023-03-03 19:34:44,953 - mmseg - INFO - Iter [69100/80000] lr: 2.344e-06, eta: 0:39:51, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2067, decode.acc_seg: 91.6543, loss: 0.2067 2023-03-03 19:34:54,834 - mmseg - INFO - Iter [69150/80000] lr: 2.344e-06, eta: 0:39:40, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2048, decode.acc_seg: 91.8546, loss: 0.2048 2023-03-03 19:35:04,873 - mmseg - INFO - Iter [69200/80000] lr: 2.344e-06, eta: 0:39:29, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2245, decode.acc_seg: 91.1983, loss: 0.2245 2023-03-03 19:35:14,894 - mmseg - INFO - Iter [69250/80000] lr: 2.344e-06, eta: 0:39:18, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.7695, loss: 0.2060 2023-03-03 19:35:25,048 - mmseg - INFO - Iter [69300/80000] lr: 2.344e-06, eta: 0:39:07, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2200, decode.acc_seg: 91.2976, loss: 0.2200 2023-03-03 19:35:35,187 - mmseg - INFO - Iter [69350/80000] lr: 2.344e-06, eta: 0:38:56, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2133, decode.acc_seg: 91.5117, loss: 0.2133 2023-03-03 19:35:45,049 - mmseg - INFO - Iter [69400/80000] lr: 2.344e-06, eta: 0:38:44, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2088, decode.acc_seg: 91.6076, loss: 0.2088 2023-03-03 19:35:57,743 - mmseg - INFO - Iter [69450/80000] lr: 2.344e-06, eta: 0:38:34, time: 0.254, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2122, decode.acc_seg: 91.5148, loss: 0.2122 2023-03-03 19:36:07,726 - mmseg - INFO - Iter [69500/80000] lr: 2.344e-06, eta: 0:38:23, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2061, decode.acc_seg: 91.6626, loss: 0.2061 2023-03-03 19:36:17,827 - mmseg - INFO - Iter [69550/80000] lr: 2.344e-06, eta: 0:38:12, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2109, decode.acc_seg: 91.6635, loss: 0.2109 2023-03-03 19:36:27,932 - mmseg - INFO - Iter [69600/80000] lr: 2.344e-06, eta: 0:38:00, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2131, decode.acc_seg: 91.7371, loss: 0.2131 2023-03-03 19:36:37,868 - mmseg - INFO - Iter [69650/80000] lr: 2.344e-06, eta: 0:37:49, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2037, decode.acc_seg: 91.8028, loss: 0.2037 2023-03-03 19:36:48,273 - mmseg - INFO - Iter [69700/80000] lr: 2.344e-06, eta: 0:37:38, time: 0.208, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2130, decode.acc_seg: 91.4287, loss: 0.2130 2023-03-03 19:36:58,292 - mmseg - INFO - Iter [69750/80000] lr: 2.344e-06, eta: 0:37:27, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2056, decode.acc_seg: 91.7815, loss: 0.2056 2023-03-03 19:37:08,167 - mmseg - INFO - Iter [69800/80000] lr: 2.344e-06, eta: 0:37:16, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2061, decode.acc_seg: 91.9116, loss: 0.2061 2023-03-03 19:37:18,159 - mmseg - INFO - Iter [69850/80000] lr: 2.344e-06, eta: 0:37:05, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2106, decode.acc_seg: 91.4940, loss: 0.2106 2023-03-03 19:37:28,184 - mmseg - INFO - Iter [69900/80000] lr: 2.344e-06, eta: 0:36:54, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2068, decode.acc_seg: 91.6620, loss: 0.2068 2023-03-03 19:37:38,071 - mmseg - INFO - Iter [69950/80000] lr: 2.344e-06, eta: 0:36:43, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2112, decode.acc_seg: 91.3627, loss: 0.2112 2023-03-03 19:37:48,105 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:37:48,105 - mmseg - INFO - Iter [70000/80000] lr: 2.344e-06, eta: 0:36:32, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2197, decode.acc_seg: 91.2469, loss: 0.2197 2023-03-03 19:38:00,575 - mmseg - INFO - Iter [70050/80000] lr: 1.172e-06, eta: 0:36:21, time: 0.249, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2065, decode.acc_seg: 91.7109, loss: 0.2065 2023-03-03 19:38:10,507 - mmseg - INFO - Iter [70100/80000] lr: 1.172e-06, eta: 0:36:10, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2054, decode.acc_seg: 91.6418, loss: 0.2054 2023-03-03 19:38:20,485 - mmseg - INFO - Iter [70150/80000] lr: 1.172e-06, eta: 0:35:59, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2097, decode.acc_seg: 91.5580, loss: 0.2097 2023-03-03 19:38:30,529 - mmseg - INFO - Iter [70200/80000] lr: 1.172e-06, eta: 0:35:48, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2070, decode.acc_seg: 91.7340, loss: 0.2070 2023-03-03 19:38:40,520 - mmseg - INFO - Iter [70250/80000] lr: 1.172e-06, eta: 0:35:37, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.5065, loss: 0.2115 2023-03-03 19:38:50,433 - mmseg - INFO - Iter [70300/80000] lr: 1.172e-06, eta: 0:35:25, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2023, decode.acc_seg: 91.7540, loss: 0.2023 2023-03-03 19:39:00,390 - mmseg - INFO - Iter [70350/80000] lr: 1.172e-06, eta: 0:35:14, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2056, decode.acc_seg: 91.7356, loss: 0.2056 2023-03-03 19:39:10,678 - mmseg - INFO - Iter [70400/80000] lr: 1.172e-06, eta: 0:35:03, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2118, decode.acc_seg: 91.4726, loss: 0.2118 2023-03-03 19:39:20,609 - mmseg - INFO - Iter [70450/80000] lr: 1.172e-06, eta: 0:34:52, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.4965, loss: 0.2125 2023-03-03 19:39:30,747 - mmseg - INFO - Iter [70500/80000] lr: 1.172e-06, eta: 0:34:41, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2129, decode.acc_seg: 91.4071, loss: 0.2129 2023-03-03 19:39:40,733 - mmseg - INFO - Iter [70550/80000] lr: 1.172e-06, eta: 0:34:30, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2096, decode.acc_seg: 91.6852, loss: 0.2096 2023-03-03 19:39:50,619 - mmseg - INFO - Iter [70600/80000] lr: 1.172e-06, eta: 0:34:19, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2049, decode.acc_seg: 91.8179, loss: 0.2049 2023-03-03 19:40:00,791 - mmseg - INFO - Iter [70650/80000] lr: 1.172e-06, eta: 0:34:08, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2073, decode.acc_seg: 91.5777, loss: 0.2073 2023-03-03 19:40:13,258 - mmseg - INFO - Iter [70700/80000] lr: 1.172e-06, eta: 0:33:57, time: 0.249, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2119, decode.acc_seg: 91.4436, loss: 0.2119 2023-03-03 19:40:23,236 - mmseg - INFO - Iter [70750/80000] lr: 1.172e-06, eta: 0:33:46, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.6415, loss: 0.2108 2023-03-03 19:40:33,181 - mmseg - INFO - Iter [70800/80000] lr: 1.172e-06, eta: 0:33:35, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.5717, loss: 0.2117 2023-03-03 19:40:43,156 - mmseg - INFO - Iter [70850/80000] lr: 1.172e-06, eta: 0:33:24, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.6900, loss: 0.2081 2023-03-03 19:40:53,205 - mmseg - INFO - Iter [70900/80000] lr: 1.172e-06, eta: 0:33:13, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2148, decode.acc_seg: 91.4353, loss: 0.2148 2023-03-03 19:41:03,351 - mmseg - INFO - Iter [70950/80000] lr: 1.172e-06, eta: 0:33:02, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.6392, loss: 0.2090 2023-03-03 19:41:13,451 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:41:13,452 - mmseg - INFO - Iter [71000/80000] lr: 1.172e-06, eta: 0:32:51, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2152, decode.acc_seg: 91.4452, loss: 0.2152 2023-03-03 19:41:23,613 - mmseg - INFO - Iter [71050/80000] lr: 1.172e-06, eta: 0:32:40, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2018, decode.acc_seg: 91.8363, loss: 0.2018 2023-03-03 19:41:33,549 - mmseg - INFO - Iter [71100/80000] lr: 1.172e-06, eta: 0:32:29, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1988, decode.acc_seg: 91.9514, loss: 0.1988 2023-03-03 19:41:43,621 - mmseg - INFO - Iter [71150/80000] lr: 1.172e-06, eta: 0:32:17, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.7452, loss: 0.2080 2023-03-03 19:41:53,641 - mmseg - INFO - Iter [71200/80000] lr: 1.172e-06, eta: 0:32:06, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2044, decode.acc_seg: 91.7228, loss: 0.2044 2023-03-03 19:42:03,635 - mmseg - INFO - Iter [71250/80000] lr: 1.172e-06, eta: 0:31:55, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2033, decode.acc_seg: 91.7240, loss: 0.2033 2023-03-03 19:42:13,598 - mmseg - INFO - Iter [71300/80000] lr: 1.172e-06, eta: 0:31:44, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2142, decode.acc_seg: 91.4506, loss: 0.2142 2023-03-03 19:42:26,250 - mmseg - INFO - Iter [71350/80000] lr: 1.172e-06, eta: 0:31:34, time: 0.253, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2114, decode.acc_seg: 91.6983, loss: 0.2114 2023-03-03 19:42:36,140 - mmseg - INFO - Iter [71400/80000] lr: 1.172e-06, eta: 0:31:22, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2036, decode.acc_seg: 91.9131, loss: 0.2036 2023-03-03 19:42:46,036 - mmseg - INFO - Iter [71450/80000] lr: 1.172e-06, eta: 0:31:11, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.3292, loss: 0.2151 2023-03-03 19:42:56,046 - mmseg - INFO - Iter [71500/80000] lr: 1.172e-06, eta: 0:31:00, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2132, decode.acc_seg: 91.4951, loss: 0.2132 2023-03-03 19:43:06,086 - mmseg - INFO - Iter [71550/80000] lr: 1.172e-06, eta: 0:30:49, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.5274, loss: 0.2145 2023-03-03 19:43:16,499 - mmseg - INFO - Iter [71600/80000] lr: 1.172e-06, eta: 0:30:38, time: 0.208, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2098, decode.acc_seg: 91.5389, loss: 0.2098 2023-03-03 19:43:26,377 - mmseg - INFO - Iter [71650/80000] lr: 1.172e-06, eta: 0:30:27, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2103, decode.acc_seg: 91.4894, loss: 0.2103 2023-03-03 19:43:36,301 - mmseg - INFO - Iter [71700/80000] lr: 1.172e-06, eta: 0:30:16, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2134, decode.acc_seg: 91.3685, loss: 0.2134 2023-03-03 19:43:46,323 - mmseg - INFO - Iter [71750/80000] lr: 1.172e-06, eta: 0:30:05, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2059, decode.acc_seg: 91.7297, loss: 0.2059 2023-03-03 19:43:56,277 - mmseg - INFO - Iter [71800/80000] lr: 1.172e-06, eta: 0:29:54, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1992, decode.acc_seg: 92.0092, loss: 0.1992 2023-03-03 19:44:06,268 - mmseg - INFO - Iter [71850/80000] lr: 1.172e-06, eta: 0:29:43, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2009, decode.acc_seg: 92.0041, loss: 0.2009 2023-03-03 19:44:16,259 - mmseg - INFO - Iter [71900/80000] lr: 1.172e-06, eta: 0:29:32, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2157, decode.acc_seg: 91.2752, loss: 0.2157 2023-03-03 19:44:28,875 - mmseg - INFO - Iter [71950/80000] lr: 1.172e-06, eta: 0:29:21, time: 0.252, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2121, decode.acc_seg: 91.5876, loss: 0.2121 2023-03-03 19:44:38,858 - mmseg - INFO - Saving checkpoint at 72000 iterations 2023-03-03 19:44:39,689 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:44:39,689 - mmseg - INFO - Iter [72000/80000] lr: 1.172e-06, eta: 0:29:10, time: 0.216, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.4199, loss: 0.2080 2023-03-03 19:44:54,436 - mmseg - INFO - per class results: 2023-03-03 19:44:54,442 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 75.9 | 88.51 | | building | 82.31 | 93.53 | | sky | 94.11 | 97.25 | | floor | 79.01 | 90.35 | | tree | 73.32 | 87.81 | | ceiling | 82.27 | 90.85 | | road | 80.37 | 87.65 | | bed | 87.68 | 95.29 | | windowpane | 59.26 | 76.42 | | grass | 65.25 | 83.25 | | cabinet | 57.84 | 71.26 | | sidewalk | 63.4 | 80.99 | | person | 77.95 | 90.92 | | earth | 32.3 | 45.8 | | door | 45.54 | 59.77 | | table | 59.82 | 75.89 | | mountain | 50.78 | 66.89 | | plant | 50.92 | 64.52 | | curtain | 71.23 | 82.66 | | chair | 54.96 | 70.14 | | car | 81.38 | 90.26 | | water | 45.65 | 60.78 | | painting | 70.94 | 85.81 | | sofa | 63.53 | 82.81 | | shelf | 38.79 | 55.11 | | house | 46.4 | 54.7 | | sea | 43.04 | 68.95 | | mirror | 63.26 | 71.74 | | rug | 57.27 | 63.24 | | field | 22.72 | 35.75 | | armchair | 41.43 | 56.73 | | seat | 57.92 | 77.79 | | fence | 34.0 | 45.36 | | desk | 48.96 | 67.58 | | rock | 27.32 | 41.76 | | wardrobe | 45.49 | 62.4 | | lamp | 62.46 | 74.99 | | bathtub | 74.37 | 80.42 | | railing | 28.04 | 41.64 | | cushion | 53.11 | 65.29 | | base | 20.61 | 31.14 | | box | 20.88 | 25.89 | | column | 44.77 | 55.93 | | signboard | 35.56 | 47.14 | | chest of drawers | 37.41 | 58.13 | | counter | 26.98 | 33.6 | | sand | 31.44 | 48.36 | | sink | 66.61 | 78.62 | | skyscraper | 61.48 | 68.83 | | fireplace | 69.78 | 87.58 | | refrigerator | 69.2 | 81.19 | | grandstand | 39.91 | 61.51 | | path | 14.71 | 21.15 | | stairs | 30.13 | 38.1 | | runway | 61.68 | 81.64 | | case | 44.46 | 67.51 | | pool table | 91.51 | 95.68 | | pillow | 54.23 | 66.43 | | screen door | 67.99 | 75.96 | | stairway | 30.9 | 39.67 | | river | 12.15 | 22.15 | | bridge | 59.71 | 65.2 | | bookcase | 39.12 | 49.35 | | blind | 40.18 | 46.21 | | coffee table | 58.16 | 77.34 | | toilet | 85.56 | 90.74 | | flower | 34.7 | 45.86 | | book | 44.99 | 62.84 | | hill | 5.12 | 6.94 | | bench | 37.66 | 49.69 | | countertop | 55.67 | 73.2 | | stove | 72.53 | 80.92 | | palm | 51.17 | 71.14 | | kitchen island | 45.7 | 75.2 | | computer | 54.67 | 64.61 | | swivel chair | 43.68 | 56.81 | | boat | 48.18 | 55.67 | | bar | 24.95 | 30.17 | | arcade machine | 21.59 | 23.52 | | hovel | 37.28 | 40.25 | | bus | 79.16 | 87.31 | | towel | 56.94 | 66.44 | | light | 52.99 | 58.85 | | truck | 31.65 | 41.87 | | tower | 30.86 | 36.97 | | chandelier | 67.81 | 83.05 | | awning | 24.13 | 26.77 | | streetlight | 25.46 | 31.04 | | booth | 42.5 | 44.94 | | television receiver | 66.15 | 78.26 | | airplane | 49.56 | 62.23 | | dirt track | 2.03 | 4.59 | | apparel | 28.93 | 42.12 | | pole | 23.85 | 35.74 | | land | 0.82 | 1.15 | | bannister | 11.1 | 14.43 | | escalator | 21.39 | 22.31 | | ottoman | 43.61 | 56.04 | | bottle | 12.58 | 20.19 | | buffet | 34.08 | 43.73 | | poster | 22.27 | 28.53 | | stage | 10.68 | 13.26 | | van | 41.45 | 57.65 | | ship | 65.4 | 74.32 | | fountain | 0.57 | 0.57 | | conveyer belt | 63.34 | 86.3 | | canopy | 14.7 | 17.22 | | washer | 63.4 | 65.47 | | plaything | 20.75 | 24.01 | | swimming pool | 28.51 | 34.38 | | stool | 41.48 | 52.66 | | barrel | 36.84 | 64.83 | | basket | 22.8 | 33.53 | | waterfall | 58.18 | 80.3 | | tent | 92.25 | 98.38 | | bag | 8.59 | 10.21 | | minibike | 49.15 | 55.79 | | cradle | 76.01 | 96.95 | | oven | 22.67 | 56.26 | | ball | 47.08 | 66.49 | | food | 50.96 | 60.81 | | step | 3.89 | 4.97 | | tank | 45.03 | 45.5 | | trade name | 21.23 | 24.32 | | microwave | 38.81 | 41.68 | | pot | 36.18 | 42.09 | | animal | 49.82 | 51.46 | | bicycle | 45.44 | 70.79 | | lake | 61.78 | 62.94 | | dishwasher | 72.03 | 76.14 | | screen | 59.68 | 71.61 | | blanket | 6.82 | 7.91 | | sculpture | 40.14 | 62.9 | | hood | 61.58 | 69.46 | | sconce | 40.3 | 46.09 | | vase | 32.2 | 47.74 | | traffic light | 26.74 | 37.65 | | tray | 5.57 | 8.53 | | ashcan | 40.53 | 52.92 | | fan | 56.05 | 69.48 | | pier | 24.21 | 31.66 | | crt screen | 4.12 | 7.73 | | plate | 39.4 | 50.37 | | monitor | 62.34 | 73.14 | | bulletin board | 35.55 | 49.63 | | shower | 1.23 | 1.67 | | radiator | 41.13 | 49.86 | | glass | 10.02 | 11.16 | | clock | 18.44 | 22.37 | | flag | 37.93 | 40.11 | +---------------------+-------+-------+ 2023-03-03 19:44:54,442 - mmseg - INFO - Summary: 2023-03-03 19:44:54,442 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.52 | 44.69 | 55.35 | +-------+-------+-------+ 2023-03-03 19:44:54,443 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:44:54,443 - mmseg - INFO - Iter(val) [250] aAcc: 0.8152, mIoU: 0.4469, mAcc: 0.5535, IoU.background: nan, IoU.wall: 0.7590, IoU.building: 0.8231, IoU.sky: 0.9411, IoU.floor: 0.7901, IoU.tree: 0.7332, IoU.ceiling: 0.8227, IoU.road: 0.8037, IoU.bed : 0.8768, IoU.windowpane: 0.5926, IoU.grass: 0.6525, IoU.cabinet: 0.5784, IoU.sidewalk: 0.6340, IoU.person: 0.7795, IoU.earth: 0.3230, IoU.door: 0.4554, IoU.table: 0.5982, IoU.mountain: 0.5078, IoU.plant: 0.5092, IoU.curtain: 0.7123, IoU.chair: 0.5496, IoU.car: 0.8138, IoU.water: 0.4565, IoU.painting: 0.7094, IoU.sofa: 0.6353, IoU.shelf: 0.3879, IoU.house: 0.4640, IoU.sea: 0.4304, IoU.mirror: 0.6326, IoU.rug: 0.5727, IoU.field: 0.2272, IoU.armchair: 0.4143, IoU.seat: 0.5792, IoU.fence: 0.3400, IoU.desk: 0.4896, IoU.rock: 0.2732, IoU.wardrobe: 0.4549, IoU.lamp: 0.6246, IoU.bathtub: 0.7437, IoU.railing: 0.2804, IoU.cushion: 0.5311, IoU.base: 0.2061, IoU.box: 0.2088, IoU.column: 0.4477, IoU.signboard: 0.3556, IoU.chest of drawers: 0.3741, IoU.counter: 0.2698, IoU.sand: 0.3144, IoU.sink: 0.6661, IoU.skyscraper: 0.6148, IoU.fireplace: 0.6978, IoU.refrigerator: 0.6920, IoU.grandstand: 0.3991, IoU.path: 0.1471, IoU.stairs: 0.3013, IoU.runway: 0.6168, IoU.case: 0.4446, IoU.pool table: 0.9151, IoU.pillow: 0.5423, IoU.screen door: 0.6799, IoU.stairway: 0.3090, IoU.river: 0.1215, IoU.bridge: 0.5971, IoU.bookcase: 0.3912, IoU.blind: 0.4018, IoU.coffee table: 0.5816, IoU.toilet: 0.8556, IoU.flower: 0.3470, IoU.book: 0.4499, IoU.hill: 0.0512, IoU.bench: 0.3766, IoU.countertop: 0.5567, IoU.stove: 0.7253, IoU.palm: 0.5117, IoU.kitchen island: 0.4570, IoU.computer: 0.5467, IoU.swivel chair: 0.4368, IoU.boat: 0.4818, IoU.bar: 0.2495, IoU.arcade machine: 0.2159, IoU.hovel: 0.3728, IoU.bus: 0.7916, IoU.towel: 0.5694, IoU.light: 0.5299, IoU.truck: 0.3165, IoU.tower: 0.3086, IoU.chandelier: 0.6781, IoU.awning: 0.2413, IoU.streetlight: 0.2546, IoU.booth: 0.4250, IoU.television receiver: 0.6615, IoU.airplane: 0.4956, IoU.dirt track: 0.0203, IoU.apparel: 0.2893, IoU.pole: 0.2385, IoU.land: 0.0082, IoU.bannister: 0.1110, IoU.escalator: 0.2139, IoU.ottoman: 0.4361, IoU.bottle: 0.1258, IoU.buffet: 0.3408, IoU.poster: 0.2227, IoU.stage: 0.1068, IoU.van: 0.4145, IoU.ship: 0.6540, IoU.fountain: 0.0057, IoU.conveyer belt: 0.6334, IoU.canopy: 0.1470, IoU.washer: 0.6340, IoU.plaything: 0.2075, IoU.swimming pool: 0.2851, IoU.stool: 0.4148, IoU.barrel: 0.3684, IoU.basket: 0.2280, IoU.waterfall: 0.5818, IoU.tent: 0.9225, IoU.bag: 0.0859, IoU.minibike: 0.4915, IoU.cradle: 0.7601, IoU.oven: 0.2267, IoU.ball: 0.4708, IoU.food: 0.5096, IoU.step: 0.0389, IoU.tank: 0.4503, IoU.trade name: 0.2123, IoU.microwave: 0.3881, IoU.pot: 0.3618, IoU.animal: 0.4982, IoU.bicycle: 0.4544, IoU.lake: 0.6178, IoU.dishwasher: 0.7203, IoU.screen: 0.5968, IoU.blanket: 0.0682, IoU.sculpture: 0.4014, IoU.hood: 0.6158, IoU.sconce: 0.4030, IoU.vase: 0.3220, IoU.traffic light: 0.2674, IoU.tray: 0.0557, IoU.ashcan: 0.4053, IoU.fan: 0.5605, IoU.pier: 0.2421, IoU.crt screen: 0.0412, IoU.plate: 0.3940, IoU.monitor: 0.6234, IoU.bulletin board: 0.3555, IoU.shower: 0.0123, IoU.radiator: 0.4113, IoU.glass: 0.1002, IoU.clock: 0.1844, IoU.flag: 0.3793, Acc.background: nan, Acc.wall: 0.8851, Acc.building: 0.9353, Acc.sky: 0.9725, Acc.floor: 0.9035, Acc.tree: 0.8781, Acc.ceiling: 0.9085, Acc.road: 0.8765, Acc.bed : 0.9529, Acc.windowpane: 0.7642, Acc.grass: 0.8325, Acc.cabinet: 0.7126, Acc.sidewalk: 0.8099, Acc.person: 0.9092, Acc.earth: 0.4580, Acc.door: 0.5977, Acc.table: 0.7589, Acc.mountain: 0.6689, Acc.plant: 0.6452, Acc.curtain: 0.8266, Acc.chair: 0.7014, Acc.car: 0.9026, Acc.water: 0.6078, Acc.painting: 0.8581, Acc.sofa: 0.8281, Acc.shelf: 0.5511, Acc.house: 0.5470, Acc.sea: 0.6895, Acc.mirror: 0.7174, Acc.rug: 0.6324, Acc.field: 0.3575, Acc.armchair: 0.5673, Acc.seat: 0.7779, Acc.fence: 0.4536, Acc.desk: 0.6758, Acc.rock: 0.4176, Acc.wardrobe: 0.6240, Acc.lamp: 0.7499, Acc.bathtub: 0.8042, Acc.railing: 0.4164, Acc.cushion: 0.6529, Acc.base: 0.3114, Acc.box: 0.2589, Acc.column: 0.5593, Acc.signboard: 0.4714, Acc.chest of drawers: 0.5813, Acc.counter: 0.3360, Acc.sand: 0.4836, Acc.sink: 0.7862, Acc.skyscraper: 0.6883, Acc.fireplace: 0.8758, Acc.refrigerator: 0.8119, Acc.grandstand: 0.6151, Acc.path: 0.2115, Acc.stairs: 0.3810, Acc.runway: 0.8164, Acc.case: 0.6751, Acc.pool table: 0.9568, Acc.pillow: 0.6643, Acc.screen door: 0.7596, Acc.stairway: 0.3967, Acc.river: 0.2215, Acc.bridge: 0.6520, Acc.bookcase: 0.4935, Acc.blind: 0.4621, Acc.coffee table: 0.7734, Acc.toilet: 0.9074, Acc.flower: 0.4586, Acc.book: 0.6284, Acc.hill: 0.0694, Acc.bench: 0.4969, Acc.countertop: 0.7320, Acc.stove: 0.8092, Acc.palm: 0.7114, Acc.kitchen island: 0.7520, Acc.computer: 0.6461, Acc.swivel chair: 0.5681, Acc.boat: 0.5567, Acc.bar: 0.3017, Acc.arcade machine: 0.2352, Acc.hovel: 0.4025, Acc.bus: 0.8731, Acc.towel: 0.6644, Acc.light: 0.5885, Acc.truck: 0.4187, Acc.tower: 0.3697, Acc.chandelier: 0.8305, Acc.awning: 0.2677, Acc.streetlight: 0.3104, Acc.booth: 0.4494, Acc.television receiver: 0.7826, Acc.airplane: 0.6223, Acc.dirt track: 0.0459, Acc.apparel: 0.4212, Acc.pole: 0.3574, Acc.land: 0.0115, Acc.bannister: 0.1443, Acc.escalator: 0.2231, Acc.ottoman: 0.5604, Acc.bottle: 0.2019, Acc.buffet: 0.4373, Acc.poster: 0.2853, Acc.stage: 0.1326, Acc.van: 0.5765, Acc.ship: 0.7432, Acc.fountain: 0.0057, Acc.conveyer belt: 0.8630, Acc.canopy: 0.1722, Acc.washer: 0.6547, Acc.plaything: 0.2401, Acc.swimming pool: 0.3438, Acc.stool: 0.5266, Acc.barrel: 0.6483, Acc.basket: 0.3353, Acc.waterfall: 0.8030, Acc.tent: 0.9838, Acc.bag: 0.1021, Acc.minibike: 0.5579, Acc.cradle: 0.9695, Acc.oven: 0.5626, Acc.ball: 0.6649, Acc.food: 0.6081, Acc.step: 0.0497, Acc.tank: 0.4550, Acc.trade name: 0.2432, Acc.microwave: 0.4168, Acc.pot: 0.4209, Acc.animal: 0.5146, Acc.bicycle: 0.7079, Acc.lake: 0.6294, Acc.dishwasher: 0.7614, Acc.screen: 0.7161, Acc.blanket: 0.0791, Acc.sculpture: 0.6290, Acc.hood: 0.6946, Acc.sconce: 0.4609, Acc.vase: 0.4774, Acc.traffic light: 0.3765, Acc.tray: 0.0853, Acc.ashcan: 0.5292, Acc.fan: 0.6948, Acc.pier: 0.3166, Acc.crt screen: 0.0773, Acc.plate: 0.5037, Acc.monitor: 0.7314, Acc.bulletin board: 0.4963, Acc.shower: 0.0167, Acc.radiator: 0.4986, Acc.glass: 0.1116, Acc.clock: 0.2237, Acc.flag: 0.4011 2023-03-03 19:45:04,888 - mmseg - INFO - Iter [72050/80000] lr: 1.172e-06, eta: 0:29:01, time: 0.504, data_time: 0.302, memory: 67202, decode.loss_ce: 0.2095, decode.acc_seg: 91.6862, loss: 0.2095 2023-03-03 19:45:15,013 - mmseg - INFO - Iter [72100/80000] lr: 1.172e-06, eta: 0:28:50, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2076, decode.acc_seg: 91.5845, loss: 0.2076 2023-03-03 19:45:25,022 - mmseg - INFO - Iter [72150/80000] lr: 1.172e-06, eta: 0:28:39, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2055, decode.acc_seg: 91.8392, loss: 0.2055 2023-03-03 19:45:35,059 - mmseg - INFO - Iter [72200/80000] lr: 1.172e-06, eta: 0:28:28, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2054, decode.acc_seg: 91.7610, loss: 0.2054 2023-03-03 19:45:44,957 - mmseg - INFO - Iter [72250/80000] lr: 1.172e-06, eta: 0:28:17, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2196, decode.acc_seg: 91.2797, loss: 0.2196 2023-03-03 19:45:55,097 - mmseg - INFO - Iter [72300/80000] lr: 1.172e-06, eta: 0:28:06, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.6342, loss: 0.2153 2023-03-03 19:46:05,181 - mmseg - INFO - Iter [72350/80000] lr: 1.172e-06, eta: 0:27:55, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2084, decode.acc_seg: 91.6646, loss: 0.2084 2023-03-03 19:46:15,324 - mmseg - INFO - Iter [72400/80000] lr: 1.172e-06, eta: 0:27:44, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.5120, loss: 0.2092 2023-03-03 19:46:25,631 - mmseg - INFO - Iter [72450/80000] lr: 1.172e-06, eta: 0:27:33, time: 0.206, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.5233, loss: 0.2135 2023-03-03 19:46:35,672 - mmseg - INFO - Iter [72500/80000] lr: 1.172e-06, eta: 0:27:21, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1994, decode.acc_seg: 91.8975, loss: 0.1994 2023-03-03 19:46:45,672 - mmseg - INFO - Iter [72550/80000] lr: 1.172e-06, eta: 0:27:10, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2111, decode.acc_seg: 91.5363, loss: 0.2111 2023-03-03 19:46:58,167 - mmseg - INFO - Iter [72600/80000] lr: 1.172e-06, eta: 0:27:00, time: 0.250, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2048, decode.acc_seg: 91.7011, loss: 0.2048 2023-03-03 19:47:08,768 - mmseg - INFO - Iter [72650/80000] lr: 1.172e-06, eta: 0:26:49, time: 0.212, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.2674, loss: 0.2151 2023-03-03 19:47:18,962 - mmseg - INFO - Iter [72700/80000] lr: 1.172e-06, eta: 0:26:38, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.5697, loss: 0.2092 2023-03-03 19:47:28,905 - mmseg - INFO - Iter [72750/80000] lr: 1.172e-06, eta: 0:26:27, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2211, decode.acc_seg: 91.1131, loss: 0.2211 2023-03-03 19:47:39,001 - mmseg - INFO - Iter [72800/80000] lr: 1.172e-06, eta: 0:26:16, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2029, decode.acc_seg: 91.8467, loss: 0.2029 2023-03-03 19:47:49,145 - mmseg - INFO - Iter [72850/80000] lr: 1.172e-06, eta: 0:26:05, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2067, decode.acc_seg: 91.6016, loss: 0.2067 2023-03-03 19:47:59,068 - mmseg - INFO - Iter [72900/80000] lr: 1.172e-06, eta: 0:25:54, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2066, decode.acc_seg: 91.5811, loss: 0.2066 2023-03-03 19:48:09,117 - mmseg - INFO - Iter [72950/80000] lr: 1.172e-06, eta: 0:25:42, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2018, decode.acc_seg: 91.8736, loss: 0.2018 2023-03-03 19:48:18,978 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:48:18,978 - mmseg - INFO - Iter [73000/80000] lr: 1.172e-06, eta: 0:25:31, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2045, decode.acc_seg: 91.8758, loss: 0.2045 2023-03-03 19:48:29,026 - mmseg - INFO - Iter [73050/80000] lr: 1.172e-06, eta: 0:25:20, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2112, decode.acc_seg: 91.6486, loss: 0.2112 2023-03-03 19:48:39,132 - mmseg - INFO - Iter [73100/80000] lr: 1.172e-06, eta: 0:25:09, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.5715, loss: 0.2080 2023-03-03 19:48:49,110 - mmseg - INFO - Iter [73150/80000] lr: 1.172e-06, eta: 0:24:58, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2017, decode.acc_seg: 91.9202, loss: 0.2017 2023-03-03 19:49:01,571 - mmseg - INFO - Iter [73200/80000] lr: 1.172e-06, eta: 0:24:48, time: 0.249, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2181, decode.acc_seg: 91.2801, loss: 0.2181 2023-03-03 19:49:11,673 - mmseg - INFO - Iter [73250/80000] lr: 1.172e-06, eta: 0:24:37, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2131, decode.acc_seg: 91.5911, loss: 0.2131 2023-03-03 19:49:21,737 - mmseg - INFO - Iter [73300/80000] lr: 1.172e-06, eta: 0:24:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2103, decode.acc_seg: 91.3716, loss: 0.2103 2023-03-03 19:49:31,770 - mmseg - INFO - Iter [73350/80000] lr: 1.172e-06, eta: 0:24:14, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2100, decode.acc_seg: 91.5709, loss: 0.2100 2023-03-03 19:49:41,785 - mmseg - INFO - Iter [73400/80000] lr: 1.172e-06, eta: 0:24:03, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.5730, loss: 0.2080 2023-03-03 19:49:51,730 - mmseg - INFO - Iter [73450/80000] lr: 1.172e-06, eta: 0:23:52, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2104, decode.acc_seg: 91.5878, loss: 0.2104 2023-03-03 19:50:01,867 - mmseg - INFO - Iter [73500/80000] lr: 1.172e-06, eta: 0:23:41, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2094, decode.acc_seg: 91.7773, loss: 0.2094 2023-03-03 19:50:11,739 - mmseg - INFO - Iter [73550/80000] lr: 1.172e-06, eta: 0:23:30, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.6723, loss: 0.2090 2023-03-03 19:50:21,604 - mmseg - INFO - Iter [73600/80000] lr: 1.172e-06, eta: 0:23:19, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2212, decode.acc_seg: 91.3271, loss: 0.2212 2023-03-03 19:50:31,621 - mmseg - INFO - Iter [73650/80000] lr: 1.172e-06, eta: 0:23:08, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2056, decode.acc_seg: 91.7845, loss: 0.2056 2023-03-03 19:50:41,590 - mmseg - INFO - Iter [73700/80000] lr: 1.172e-06, eta: 0:22:57, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2001, decode.acc_seg: 91.9017, loss: 0.2001 2023-03-03 19:50:51,580 - mmseg - INFO - Iter [73750/80000] lr: 1.172e-06, eta: 0:22:46, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.3350, loss: 0.2140 2023-03-03 19:51:01,669 - mmseg - INFO - Iter [73800/80000] lr: 1.172e-06, eta: 0:22:35, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2112, decode.acc_seg: 91.4890, loss: 0.2112 2023-03-03 19:51:14,089 - mmseg - INFO - Iter [73850/80000] lr: 1.172e-06, eta: 0:22:24, time: 0.248, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2074, decode.acc_seg: 91.7375, loss: 0.2074 2023-03-03 19:51:23,996 - mmseg - INFO - Iter [73900/80000] lr: 1.172e-06, eta: 0:22:13, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2143, decode.acc_seg: 91.4707, loss: 0.2143 2023-03-03 19:51:33,962 - mmseg - INFO - Iter [73950/80000] lr: 1.172e-06, eta: 0:22:02, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.5290, loss: 0.2099 2023-03-03 19:51:44,225 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:51:44,225 - mmseg - INFO - Iter [74000/80000] lr: 1.172e-06, eta: 0:21:51, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2076, decode.acc_seg: 91.6747, loss: 0.2076 2023-03-03 19:51:54,220 - mmseg - INFO - Iter [74050/80000] lr: 1.172e-06, eta: 0:21:40, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2132, decode.acc_seg: 91.2825, loss: 0.2132 2023-03-03 19:52:04,250 - mmseg - INFO - Iter [74100/80000] lr: 1.172e-06, eta: 0:21:29, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2044, decode.acc_seg: 91.7883, loss: 0.2044 2023-03-03 19:52:14,184 - mmseg - INFO - Iter [74150/80000] lr: 1.172e-06, eta: 0:21:18, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2017, decode.acc_seg: 91.7615, loss: 0.2017 2023-03-03 19:52:24,205 - mmseg - INFO - Iter [74200/80000] lr: 1.172e-06, eta: 0:21:07, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.6240, loss: 0.2099 2023-03-03 19:52:34,107 - mmseg - INFO - Iter [74250/80000] lr: 1.172e-06, eta: 0:20:56, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2106, decode.acc_seg: 91.4742, loss: 0.2106 2023-03-03 19:52:44,036 - mmseg - INFO - Iter [74300/80000] lr: 1.172e-06, eta: 0:20:45, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.6429, loss: 0.2092 2023-03-03 19:52:54,009 - mmseg - INFO - Iter [74350/80000] lr: 1.172e-06, eta: 0:20:34, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2069, decode.acc_seg: 91.6671, loss: 0.2069 2023-03-03 19:53:03,896 - mmseg - INFO - Iter [74400/80000] lr: 1.172e-06, eta: 0:20:23, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2157, decode.acc_seg: 91.4737, loss: 0.2157 2023-03-03 19:53:13,781 - mmseg - INFO - Iter [74450/80000] lr: 1.172e-06, eta: 0:20:12, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2101, decode.acc_seg: 91.5885, loss: 0.2101 2023-03-03 19:53:26,399 - mmseg - INFO - Iter [74500/80000] lr: 1.172e-06, eta: 0:20:02, time: 0.252, data_time: 0.054, memory: 67202, decode.loss_ce: 0.2149, decode.acc_seg: 91.3497, loss: 0.2149 2023-03-03 19:53:36,343 - mmseg - INFO - Iter [74550/80000] lr: 1.172e-06, eta: 0:19:51, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2097, decode.acc_seg: 91.5946, loss: 0.2097 2023-03-03 19:53:46,444 - mmseg - INFO - Iter [74600/80000] lr: 1.172e-06, eta: 0:19:40, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2121, decode.acc_seg: 91.4531, loss: 0.2121 2023-03-03 19:53:56,724 - mmseg - INFO - Iter [74650/80000] lr: 1.172e-06, eta: 0:19:29, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2101, decode.acc_seg: 91.6266, loss: 0.2101 2023-03-03 19:54:06,626 - mmseg - INFO - Iter [74700/80000] lr: 1.172e-06, eta: 0:19:18, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.6442, loss: 0.2128 2023-03-03 19:54:16,547 - mmseg - INFO - Iter [74750/80000] lr: 1.172e-06, eta: 0:19:07, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2071, decode.acc_seg: 91.6870, loss: 0.2071 2023-03-03 19:54:26,492 - mmseg - INFO - Iter [74800/80000] lr: 1.172e-06, eta: 0:18:56, time: 0.199, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2161, decode.acc_seg: 91.4630, loss: 0.2161 2023-03-03 19:54:36,359 - mmseg - INFO - Iter [74850/80000] lr: 1.172e-06, eta: 0:18:45, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.5527, loss: 0.2110 2023-03-03 19:54:46,398 - mmseg - INFO - Iter [74900/80000] lr: 1.172e-06, eta: 0:18:34, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2209, decode.acc_seg: 91.3143, loss: 0.2209 2023-03-03 19:54:56,479 - mmseg - INFO - Iter [74950/80000] lr: 1.172e-06, eta: 0:18:23, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2097, decode.acc_seg: 91.6339, loss: 0.2097 2023-03-03 19:55:06,480 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:55:06,480 - mmseg - INFO - Iter [75000/80000] lr: 1.172e-06, eta: 0:18:12, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1982, decode.acc_seg: 91.9648, loss: 0.1982 2023-03-03 19:55:16,764 - mmseg - INFO - Iter [75050/80000] lr: 1.172e-06, eta: 0:18:01, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2170, decode.acc_seg: 91.3419, loss: 0.2170 2023-03-03 19:55:29,423 - mmseg - INFO - Iter [75100/80000] lr: 1.172e-06, eta: 0:17:50, time: 0.253, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2082, decode.acc_seg: 91.5510, loss: 0.2082 2023-03-03 19:55:39,545 - mmseg - INFO - Iter [75150/80000] lr: 1.172e-06, eta: 0:17:39, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2008, decode.acc_seg: 91.9182, loss: 0.2008 2023-03-03 19:55:49,742 - mmseg - INFO - Iter [75200/80000] lr: 1.172e-06, eta: 0:17:28, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2056, decode.acc_seg: 91.8018, loss: 0.2056 2023-03-03 19:55:59,920 - mmseg - INFO - Iter [75250/80000] lr: 1.172e-06, eta: 0:17:17, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2126, decode.acc_seg: 91.4644, loss: 0.2126 2023-03-03 19:56:09,777 - mmseg - INFO - Iter [75300/80000] lr: 1.172e-06, eta: 0:17:06, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2019, decode.acc_seg: 91.8235, loss: 0.2019 2023-03-03 19:56:19,768 - mmseg - INFO - Iter [75350/80000] lr: 1.172e-06, eta: 0:16:55, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2115, decode.acc_seg: 91.5828, loss: 0.2115 2023-03-03 19:56:29,817 - mmseg - INFO - Iter [75400/80000] lr: 1.172e-06, eta: 0:16:44, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2156, decode.acc_seg: 91.4536, loss: 0.2156 2023-03-03 19:56:39,937 - mmseg - INFO - Iter [75450/80000] lr: 1.172e-06, eta: 0:16:33, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2099, decode.acc_seg: 91.5309, loss: 0.2099 2023-03-03 19:56:49,925 - mmseg - INFO - Iter [75500/80000] lr: 1.172e-06, eta: 0:16:22, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2110, decode.acc_seg: 91.5327, loss: 0.2110 2023-03-03 19:56:59,841 - mmseg - INFO - Iter [75550/80000] lr: 1.172e-06, eta: 0:16:11, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2164, decode.acc_seg: 91.5964, loss: 0.2164 2023-03-03 19:57:09,951 - mmseg - INFO - Iter [75600/80000] lr: 1.172e-06, eta: 0:16:00, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2155, decode.acc_seg: 91.3939, loss: 0.2155 2023-03-03 19:57:20,007 - mmseg - INFO - Iter [75650/80000] lr: 1.172e-06, eta: 0:15:49, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2108, decode.acc_seg: 91.5329, loss: 0.2108 2023-03-03 19:57:30,085 - mmseg - INFO - Iter [75700/80000] lr: 1.172e-06, eta: 0:15:38, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.7774, loss: 0.2105 2023-03-03 19:57:42,981 - mmseg - INFO - Iter [75750/80000] lr: 1.172e-06, eta: 0:15:27, time: 0.258, data_time: 0.058, memory: 67202, decode.loss_ce: 0.2085, decode.acc_seg: 91.7774, loss: 0.2085 2023-03-03 19:57:52,842 - mmseg - INFO - Iter [75800/80000] lr: 1.172e-06, eta: 0:15:17, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2127, decode.acc_seg: 91.6222, loss: 0.2127 2023-03-03 19:58:02,778 - mmseg - INFO - Iter [75850/80000] lr: 1.172e-06, eta: 0:15:06, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2076, decode.acc_seg: 91.7516, loss: 0.2076 2023-03-03 19:58:12,815 - mmseg - INFO - Iter [75900/80000] lr: 1.172e-06, eta: 0:14:55, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2105, decode.acc_seg: 91.4412, loss: 0.2105 2023-03-03 19:58:23,039 - mmseg - INFO - Iter [75950/80000] lr: 1.172e-06, eta: 0:14:44, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2075, decode.acc_seg: 91.6797, loss: 0.2075 2023-03-03 19:58:32,984 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 19:58:32,985 - mmseg - INFO - Iter [76000/80000] lr: 1.172e-06, eta: 0:14:33, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2037, decode.acc_seg: 91.8015, loss: 0.2037 2023-03-03 19:58:43,079 - mmseg - INFO - Iter [76050/80000] lr: 1.172e-06, eta: 0:14:22, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2042, decode.acc_seg: 91.7115, loss: 0.2042 2023-03-03 19:58:53,089 - mmseg - INFO - Iter [76100/80000] lr: 1.172e-06, eta: 0:14:11, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2141, decode.acc_seg: 91.4257, loss: 0.2141 2023-03-03 19:59:03,083 - mmseg - INFO - Iter [76150/80000] lr: 1.172e-06, eta: 0:14:00, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2045, decode.acc_seg: 91.6681, loss: 0.2045 2023-03-03 19:59:12,989 - mmseg - INFO - Iter [76200/80000] lr: 1.172e-06, eta: 0:13:49, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2121, decode.acc_seg: 91.3532, loss: 0.2121 2023-03-03 19:59:22,875 - mmseg - INFO - Iter [76250/80000] lr: 1.172e-06, eta: 0:13:38, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2104, decode.acc_seg: 91.6044, loss: 0.2104 2023-03-03 19:59:32,903 - mmseg - INFO - Iter [76300/80000] lr: 1.172e-06, eta: 0:13:27, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2024, decode.acc_seg: 91.8212, loss: 0.2024 2023-03-03 19:59:42,884 - mmseg - INFO - Iter [76350/80000] lr: 1.172e-06, eta: 0:13:16, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2081, decode.acc_seg: 91.6948, loss: 0.2081 2023-03-03 19:59:55,445 - mmseg - INFO - Iter [76400/80000] lr: 1.172e-06, eta: 0:13:05, time: 0.251, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2085, decode.acc_seg: 91.6884, loss: 0.2085 2023-03-03 20:00:05,364 - mmseg - INFO - Iter [76450/80000] lr: 1.172e-06, eta: 0:12:54, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2145, decode.acc_seg: 91.4507, loss: 0.2145 2023-03-03 20:00:15,660 - mmseg - INFO - Iter [76500/80000] lr: 1.172e-06, eta: 0:12:43, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2058, decode.acc_seg: 91.8594, loss: 0.2058 2023-03-03 20:00:25,536 - mmseg - INFO - Iter [76550/80000] lr: 1.172e-06, eta: 0:12:32, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.4655, loss: 0.2125 2023-03-03 20:00:35,509 - mmseg - INFO - Iter [76600/80000] lr: 1.172e-06, eta: 0:12:21, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2056, decode.acc_seg: 91.7618, loss: 0.2056 2023-03-03 20:00:45,412 - mmseg - INFO - Iter [76650/80000] lr: 1.172e-06, eta: 0:12:10, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2074, decode.acc_seg: 91.6750, loss: 0.2074 2023-03-03 20:00:55,484 - mmseg - INFO - Iter [76700/80000] lr: 1.172e-06, eta: 0:11:59, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2184, decode.acc_seg: 91.2900, loss: 0.2184 2023-03-03 20:01:05,446 - mmseg - INFO - Iter [76750/80000] lr: 1.172e-06, eta: 0:11:48, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2078, decode.acc_seg: 91.7093, loss: 0.2078 2023-03-03 20:01:15,338 - mmseg - INFO - Iter [76800/80000] lr: 1.172e-06, eta: 0:11:38, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2140, decode.acc_seg: 91.3216, loss: 0.2140 2023-03-03 20:01:25,312 - mmseg - INFO - Iter [76850/80000] lr: 1.172e-06, eta: 0:11:27, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.1933, decode.acc_seg: 92.2519, loss: 0.1933 2023-03-03 20:01:35,464 - mmseg - INFO - Iter [76900/80000] lr: 1.172e-06, eta: 0:11:16, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2067, decode.acc_seg: 91.6840, loss: 0.2067 2023-03-03 20:01:45,413 - mmseg - INFO - Iter [76950/80000] lr: 1.172e-06, eta: 0:11:05, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2047, decode.acc_seg: 91.6660, loss: 0.2047 2023-03-03 20:01:58,001 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 20:01:58,001 - mmseg - INFO - Iter [77000/80000] lr: 1.172e-06, eta: 0:10:54, time: 0.252, data_time: 0.055, memory: 67202, decode.loss_ce: 0.2129, decode.acc_seg: 91.5055, loss: 0.2129 2023-03-03 20:02:07,995 - mmseg - INFO - Iter [77050/80000] lr: 1.172e-06, eta: 0:10:43, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2227, decode.acc_seg: 91.0866, loss: 0.2227 2023-03-03 20:02:18,178 - mmseg - INFO - Iter [77100/80000] lr: 1.172e-06, eta: 0:10:32, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2061, decode.acc_seg: 91.6507, loss: 0.2061 2023-03-03 20:02:28,037 - mmseg - INFO - Iter [77150/80000] lr: 1.172e-06, eta: 0:10:21, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2058, decode.acc_seg: 91.6580, loss: 0.2058 2023-03-03 20:02:38,161 - mmseg - INFO - Iter [77200/80000] lr: 1.172e-06, eta: 0:10:10, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2095, decode.acc_seg: 91.7190, loss: 0.2095 2023-03-03 20:02:48,188 - mmseg - INFO - Iter [77250/80000] lr: 1.172e-06, eta: 0:09:59, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.6594, loss: 0.2086 2023-03-03 20:02:58,129 - mmseg - INFO - Iter [77300/80000] lr: 1.172e-06, eta: 0:09:48, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2052, decode.acc_seg: 91.6519, loss: 0.2052 2023-03-03 20:03:08,120 - mmseg - INFO - Iter [77350/80000] lr: 1.172e-06, eta: 0:09:37, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2055, decode.acc_seg: 91.6814, loss: 0.2055 2023-03-03 20:03:18,252 - mmseg - INFO - Iter [77400/80000] lr: 1.172e-06, eta: 0:09:26, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2049, decode.acc_seg: 91.7569, loss: 0.2049 2023-03-03 20:03:28,286 - mmseg - INFO - Iter [77450/80000] lr: 1.172e-06, eta: 0:09:15, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2080, decode.acc_seg: 91.6982, loss: 0.2080 2023-03-03 20:03:38,238 - mmseg - INFO - Iter [77500/80000] lr: 1.172e-06, eta: 0:09:05, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2071, decode.acc_seg: 91.7400, loss: 0.2071 2023-03-03 20:03:48,187 - mmseg - INFO - Iter [77550/80000] lr: 1.172e-06, eta: 0:08:54, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2072, decode.acc_seg: 91.5862, loss: 0.2072 2023-03-03 20:03:58,235 - mmseg - INFO - Iter [77600/80000] lr: 1.172e-06, eta: 0:08:43, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2118, decode.acc_seg: 91.6230, loss: 0.2118 2023-03-03 20:04:10,656 - mmseg - INFO - Iter [77650/80000] lr: 1.172e-06, eta: 0:08:32, time: 0.248, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2161, decode.acc_seg: 91.2969, loss: 0.2161 2023-03-03 20:04:20,568 - mmseg - INFO - Iter [77700/80000] lr: 1.172e-06, eta: 0:08:21, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2185, decode.acc_seg: 91.2195, loss: 0.2185 2023-03-03 20:04:30,625 - mmseg - INFO - Iter [77750/80000] lr: 1.172e-06, eta: 0:08:10, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2170, decode.acc_seg: 91.3527, loss: 0.2170 2023-03-03 20:04:40,615 - mmseg - INFO - Iter [77800/80000] lr: 1.172e-06, eta: 0:07:59, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2046, decode.acc_seg: 91.8148, loss: 0.2046 2023-03-03 20:04:50,995 - mmseg - INFO - Iter [77850/80000] lr: 1.172e-06, eta: 0:07:48, time: 0.208, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.6195, loss: 0.2128 2023-03-03 20:05:00,873 - mmseg - INFO - Iter [77900/80000] lr: 1.172e-06, eta: 0:07:37, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2030, decode.acc_seg: 91.7374, loss: 0.2030 2023-03-03 20:05:10,860 - mmseg - INFO - Iter [77950/80000] lr: 1.172e-06, eta: 0:07:26, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2118, decode.acc_seg: 91.5250, loss: 0.2118 2023-03-03 20:05:20,798 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 20:05:20,798 - mmseg - INFO - Iter [78000/80000] lr: 1.172e-06, eta: 0:07:15, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2094, decode.acc_seg: 91.6674, loss: 0.2094 2023-03-03 20:05:30,761 - mmseg - INFO - Iter [78050/80000] lr: 1.172e-06, eta: 0:07:04, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2088, decode.acc_seg: 91.6020, loss: 0.2088 2023-03-03 20:05:40,863 - mmseg - INFO - Iter [78100/80000] lr: 1.172e-06, eta: 0:06:54, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2012, decode.acc_seg: 91.9111, loss: 0.2012 2023-03-03 20:05:50,848 - mmseg - INFO - Iter [78150/80000] lr: 1.172e-06, eta: 0:06:43, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2128, decode.acc_seg: 91.5215, loss: 0.2128 2023-03-03 20:06:00,739 - mmseg - INFO - Iter [78200/80000] lr: 1.172e-06, eta: 0:06:32, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2063, decode.acc_seg: 91.8327, loss: 0.2063 2023-03-03 20:06:13,249 - mmseg - INFO - Iter [78250/80000] lr: 1.172e-06, eta: 0:06:21, time: 0.250, data_time: 0.056, memory: 67202, decode.loss_ce: 0.2158, decode.acc_seg: 91.4361, loss: 0.2158 2023-03-03 20:06:23,169 - mmseg - INFO - Iter [78300/80000] lr: 1.172e-06, eta: 0:06:10, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2107, decode.acc_seg: 91.6130, loss: 0.2107 2023-03-03 20:06:33,247 - mmseg - INFO - Iter [78350/80000] lr: 1.172e-06, eta: 0:05:59, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2180, decode.acc_seg: 91.3217, loss: 0.2180 2023-03-03 20:06:43,183 - mmseg - INFO - Iter [78400/80000] lr: 1.172e-06, eta: 0:05:48, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2012, decode.acc_seg: 91.9421, loss: 0.2012 2023-03-03 20:06:53,494 - mmseg - INFO - Iter [78450/80000] lr: 1.172e-06, eta: 0:05:37, time: 0.206, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2016, decode.acc_seg: 91.9192, loss: 0.2016 2023-03-03 20:07:03,528 - mmseg - INFO - Iter [78500/80000] lr: 1.172e-06, eta: 0:05:26, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2100, decode.acc_seg: 91.4350, loss: 0.2100 2023-03-03 20:07:13,401 - mmseg - INFO - Iter [78550/80000] lr: 1.172e-06, eta: 0:05:15, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2107, decode.acc_seg: 91.5222, loss: 0.2107 2023-03-03 20:07:23,335 - mmseg - INFO - Iter [78600/80000] lr: 1.172e-06, eta: 0:05:04, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2139, decode.acc_seg: 91.2734, loss: 0.2139 2023-03-03 20:07:33,322 - mmseg - INFO - Iter [78650/80000] lr: 1.172e-06, eta: 0:04:54, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.7137, loss: 0.2060 2023-03-03 20:07:43,192 - mmseg - INFO - Iter [78700/80000] lr: 1.172e-06, eta: 0:04:43, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2129, decode.acc_seg: 91.4972, loss: 0.2129 2023-03-03 20:07:53,173 - mmseg - INFO - Iter [78750/80000] lr: 1.172e-06, eta: 0:04:32, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2072, decode.acc_seg: 91.6913, loss: 0.2072 2023-03-03 20:08:03,043 - mmseg - INFO - Iter [78800/80000] lr: 1.172e-06, eta: 0:04:21, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2092, decode.acc_seg: 91.6129, loss: 0.2092 2023-03-03 20:08:13,039 - mmseg - INFO - Iter [78850/80000] lr: 1.172e-06, eta: 0:04:10, time: 0.200, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2117, decode.acc_seg: 91.6482, loss: 0.2117 2023-03-03 20:08:25,602 - mmseg - INFO - Iter [78900/80000] lr: 1.172e-06, eta: 0:03:59, time: 0.251, data_time: 0.053, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.3376, loss: 0.2125 2023-03-03 20:08:35,683 - mmseg - INFO - Iter [78950/80000] lr: 1.172e-06, eta: 0:03:48, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2064, decode.acc_seg: 91.6815, loss: 0.2064 2023-03-03 20:08:45,540 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 20:08:45,540 - mmseg - INFO - Iter [79000/80000] lr: 1.172e-06, eta: 0:03:37, time: 0.197, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2161, decode.acc_seg: 91.6030, loss: 0.2161 2023-03-03 20:08:55,771 - mmseg - INFO - Iter [79050/80000] lr: 1.172e-06, eta: 0:03:26, time: 0.205, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2116, decode.acc_seg: 91.5416, loss: 0.2116 2023-03-03 20:09:05,700 - mmseg - INFO - Iter [79100/80000] lr: 1.172e-06, eta: 0:03:15, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2085, decode.acc_seg: 91.6534, loss: 0.2085 2023-03-03 20:09:15,602 - mmseg - INFO - Iter [79150/80000] lr: 1.172e-06, eta: 0:03:05, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2159, decode.acc_seg: 91.5228, loss: 0.2159 2023-03-03 20:09:25,516 - mmseg - INFO - Iter [79200/80000] lr: 1.172e-06, eta: 0:02:54, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2147, decode.acc_seg: 91.3959, loss: 0.2147 2023-03-03 20:09:35,496 - mmseg - INFO - Iter [79250/80000] lr: 1.172e-06, eta: 0:02:43, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2037, decode.acc_seg: 91.7542, loss: 0.2037 2023-03-03 20:09:45,485 - mmseg - INFO - Iter [79300/80000] lr: 1.172e-06, eta: 0:02:32, time: 0.200, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2133, decode.acc_seg: 91.4610, loss: 0.2133 2023-03-03 20:09:55,519 - mmseg - INFO - Iter [79350/80000] lr: 1.172e-06, eta: 0:02:21, time: 0.201, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2086, decode.acc_seg: 91.6563, loss: 0.2086 2023-03-03 20:10:05,466 - mmseg - INFO - Iter [79400/80000] lr: 1.172e-06, eta: 0:02:10, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2072, decode.acc_seg: 91.7991, loss: 0.2072 2023-03-03 20:10:15,521 - mmseg - INFO - Iter [79450/80000] lr: 1.172e-06, eta: 0:01:59, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2144, decode.acc_seg: 91.4283, loss: 0.2144 2023-03-03 20:10:25,624 - mmseg - INFO - Iter [79500/80000] lr: 1.172e-06, eta: 0:01:48, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2087, decode.acc_seg: 91.6640, loss: 0.2087 2023-03-03 20:10:38,114 - mmseg - INFO - Iter [79550/80000] lr: 1.172e-06, eta: 0:01:37, time: 0.250, data_time: 0.057, memory: 67202, decode.loss_ce: 0.2076, decode.acc_seg: 91.7147, loss: 0.2076 2023-03-03 20:10:48,067 - mmseg - INFO - Iter [79600/80000] lr: 1.172e-06, eta: 0:01:27, time: 0.199, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2153, decode.acc_seg: 91.4694, loss: 0.2153 2023-03-03 20:10:58,095 - mmseg - INFO - Iter [79650/80000] lr: 1.172e-06, eta: 0:01:16, time: 0.201, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2060, decode.acc_seg: 91.7258, loss: 0.2060 2023-03-03 20:11:08,290 - mmseg - INFO - Iter [79700/80000] lr: 1.172e-06, eta: 0:01:05, time: 0.204, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2022, decode.acc_seg: 91.7858, loss: 0.2022 2023-03-03 20:11:18,423 - mmseg - INFO - Iter [79750/80000] lr: 1.172e-06, eta: 0:00:54, time: 0.203, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2151, decode.acc_seg: 91.4592, loss: 0.2151 2023-03-03 20:11:28,317 - mmseg - INFO - Iter [79800/80000] lr: 1.172e-06, eta: 0:00:43, time: 0.198, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2032, decode.acc_seg: 91.7859, loss: 0.2032 2023-03-03 20:11:38,418 - mmseg - INFO - Iter [79850/80000] lr: 1.172e-06, eta: 0:00:32, time: 0.202, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2125, decode.acc_seg: 91.5209, loss: 0.2125 2023-03-03 20:11:48,591 - mmseg - INFO - Iter [79900/80000] lr: 1.172e-06, eta: 0:00:21, time: 0.203, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2079, decode.acc_seg: 91.6974, loss: 0.2079 2023-03-03 20:11:58,696 - mmseg - INFO - Iter [79950/80000] lr: 1.172e-06, eta: 0:00:10, time: 0.202, data_time: 0.006, memory: 67202, decode.loss_ce: 0.2090, decode.acc_seg: 91.6182, loss: 0.2090 2023-03-03 20:12:08,862 - mmseg - INFO - Saving checkpoint at 80000 iterations 2023-03-03 20:12:09,791 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 20:12:09,791 - mmseg - INFO - Iter [80000/80000] lr: 1.172e-06, eta: 0:00:00, time: 0.222, data_time: 0.007, memory: 67202, decode.loss_ce: 0.2135, decode.acc_seg: 91.3747, loss: 0.2135 2023-03-03 20:12:24,362 - mmseg - INFO - per class results: 2023-03-03 20:12:24,368 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | background | nan | nan | | wall | 75.67 | 88.57 | | building | 82.4 | 93.36 | | sky | 94.13 | 97.11 | | floor | 78.95 | 90.43 | | tree | 73.17 | 88.72 | | ceiling | 82.29 | 91.45 | | road | 80.78 | 88.17 | | bed | 87.82 | 95.45 | | windowpane | 59.51 | 76.04 | | grass | 65.55 | 83.11 | | cabinet | 58.19 | 71.69 | | sidewalk | 64.17 | 80.33 | | person | 78.2 | 90.94 | | earth | 32.33 | 46.31 | | door | 45.66 | 59.61 | | table | 59.77 | 76.55 | | mountain | 51.28 | 67.07 | | plant | 50.99 | 64.23 | | curtain | 71.57 | 82.94 | | chair | 54.86 | 70.36 | | car | 81.56 | 90.48 | | water | 46.17 | 61.83 | | painting | 71.54 | 85.54 | | sofa | 63.82 | 82.04 | | shelf | 38.61 | 55.97 | | house | 45.81 | 53.31 | | sea | 43.52 | 69.07 | | mirror | 63.19 | 72.06 | | rug | 55.82 | 61.28 | | field | 22.69 | 34.26 | | armchair | 42.23 | 59.87 | | seat | 58.78 | 75.55 | | fence | 33.84 | 44.84 | | desk | 48.97 | 68.6 | | rock | 29.54 | 46.04 | | wardrobe | 45.25 | 60.88 | | lamp | 62.23 | 75.41 | | bathtub | 75.12 | 81.15 | | railing | 28.26 | 40.58 | | cushion | 52.9 | 64.81 | | base | 19.72 | 28.22 | | box | 21.56 | 27.42 | | column | 43.79 | 53.92 | | signboard | 35.92 | 49.14 | | chest of drawers | 37.73 | 56.62 | | counter | 27.39 | 33.11 | | sand | 31.21 | 48.17 | | sink | 66.78 | 78.31 | | skyscraper | 61.54 | 69.02 | | fireplace | 70.4 | 86.46 | | refrigerator | 70.0 | 81.14 | | grandstand | 39.75 | 62.18 | | path | 15.01 | 21.13 | | stairs | 30.47 | 36.75 | | runway | 60.49 | 79.8 | | case | 43.78 | 64.97 | | pool table | 91.49 | 95.68 | | pillow | 54.39 | 66.48 | | screen door | 69.8 | 78.29 | | stairway | 31.5 | 40.04 | | river | 12.1 | 22.54 | | bridge | 59.81 | 65.68 | | bookcase | 38.96 | 47.8 | | blind | 39.6 | 45.53 | | coffee table | 58.2 | 77.28 | | toilet | 85.44 | 91.0 | | flower | 35.31 | 46.26 | | book | 45.3 | 63.29 | | hill | 4.92 | 6.66 | | bench | 37.83 | 48.17 | | countertop | 55.34 | 70.71 | | stove | 72.33 | 80.54 | | palm | 51.43 | 71.2 | | kitchen island | 45.98 | 74.37 | | computer | 54.93 | 64.13 | | swivel chair | 44.49 | 59.74 | | boat | 46.5 | 56.61 | | bar | 25.43 | 29.7 | | arcade machine | 24.2 | 26.8 | | hovel | 38.97 | 43.01 | | bus | 78.94 | 87.12 | | towel | 56.13 | 66.2 | | light | 52.25 | 57.63 | | truck | 32.46 | 44.34 | | tower | 34.56 | 41.66 | | chandelier | 67.84 | 83.06 | | awning | 25.56 | 28.44 | | streetlight | 25.93 | 31.66 | | booth | 42.1 | 44.3 | | television receiver | 67.15 | 78.23 | | airplane | 48.45 | 62.88 | | dirt track | 2.14 | 5.33 | | apparel | 29.34 | 40.84 | | pole | 24.01 | 35.89 | | land | 0.76 | 1.06 | | bannister | 9.61 | 11.67 | | escalator | 21.24 | 21.97 | | ottoman | 45.43 | 59.46 | | bottle | 12.63 | 20.03 | | buffet | 33.86 | 42.62 | | poster | 20.88 | 26.57 | | stage | 8.33 | 9.95 | | van | 41.51 | 56.13 | | ship | 67.76 | 77.46 | | fountain | 0.35 | 0.35 | | conveyer belt | 60.31 | 85.25 | | canopy | 14.94 | 17.38 | | washer | 63.58 | 65.33 | | plaything | 22.59 | 28.53 | | swimming pool | 28.05 | 33.9 | | stool | 39.89 | 50.06 | | barrel | 35.79 | 64.88 | | basket | 22.31 | 33.16 | | waterfall | 59.8 | 78.61 | | tent | 92.43 | 98.38 | | bag | 8.68 | 10.3 | | minibike | 51.95 | 60.31 | | cradle | 76.15 | 96.6 | | oven | 23.84 | 55.22 | | ball | 45.46 | 66.94 | | food | 51.25 | 60.11 | | step | 2.95 | 3.39 | | tank | 43.8 | 44.43 | | trade name | 21.23 | 24.67 | | microwave | 38.37 | 41.08 | | pot | 35.96 | 41.91 | | animal | 52.38 | 54.72 | | bicycle | 45.72 | 72.13 | | lake | 61.06 | 63.02 | | dishwasher | 69.85 | 73.57 | | screen | 59.06 | 69.36 | | blanket | 7.27 | 8.52 | | sculpture | 40.74 | 65.52 | | hood | 61.96 | 69.01 | | sconce | 40.64 | 47.31 | | vase | 32.32 | 47.57 | | traffic light | 26.2 | 36.5 | | tray | 4.34 | 6.53 | | ashcan | 40.59 | 52.57 | | fan | 55.18 | 68.27 | | pier | 21.12 | 27.16 | | crt screen | 5.09 | 9.03 | | plate | 39.63 | 51.02 | | monitor | 63.34 | 73.98 | | bulletin board | 33.36 | 44.88 | | shower | 1.45 | 2.03 | | radiator | 41.42 | 48.78 | | glass | 9.67 | 10.8 | | clock | 18.61 | 23.14 | | flag | 39.12 | 41.52 | +---------------------+-------+-------+ 2023-03-03 20:12:24,368 - mmseg - INFO - Summary: 2023-03-03 20:12:24,369 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.59 | 44.77 | 55.31 | +-------+-------+-------+ 2023-03-03 20:12:24,370 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py 2023-03-03 20:12:24,370 - mmseg - INFO - Iter(val) [250] aAcc: 0.8159, mIoU: 0.4477, mAcc: 0.5531, IoU.background: nan, IoU.wall: 0.7567, IoU.building: 0.8240, IoU.sky: 0.9413, IoU.floor: 0.7895, IoU.tree: 0.7317, IoU.ceiling: 0.8229, IoU.road: 0.8078, IoU.bed : 0.8782, IoU.windowpane: 0.5951, IoU.grass: 0.6555, IoU.cabinet: 0.5819, IoU.sidewalk: 0.6417, IoU.person: 0.7820, IoU.earth: 0.3233, IoU.door: 0.4566, IoU.table: 0.5977, IoU.mountain: 0.5128, IoU.plant: 0.5099, IoU.curtain: 0.7157, IoU.chair: 0.5486, IoU.car: 0.8156, IoU.water: 0.4617, IoU.painting: 0.7154, IoU.sofa: 0.6382, IoU.shelf: 0.3861, IoU.house: 0.4581, IoU.sea: 0.4352, IoU.mirror: 0.6319, IoU.rug: 0.5582, IoU.field: 0.2269, IoU.armchair: 0.4223, IoU.seat: 0.5878, IoU.fence: 0.3384, IoU.desk: 0.4897, IoU.rock: 0.2954, IoU.wardrobe: 0.4525, IoU.lamp: 0.6223, IoU.bathtub: 0.7512, IoU.railing: 0.2826, IoU.cushion: 0.5290, IoU.base: 0.1972, IoU.box: 0.2156, IoU.column: 0.4379, IoU.signboard: 0.3592, IoU.chest of drawers: 0.3773, IoU.counter: 0.2739, IoU.sand: 0.3121, IoU.sink: 0.6678, IoU.skyscraper: 0.6154, IoU.fireplace: 0.7040, IoU.refrigerator: 0.7000, IoU.grandstand: 0.3975, IoU.path: 0.1501, IoU.stairs: 0.3047, IoU.runway: 0.6049, IoU.case: 0.4378, IoU.pool table: 0.9149, IoU.pillow: 0.5439, IoU.screen door: 0.6980, IoU.stairway: 0.3150, IoU.river: 0.1210, IoU.bridge: 0.5981, IoU.bookcase: 0.3896, IoU.blind: 0.3960, IoU.coffee table: 0.5820, IoU.toilet: 0.8544, IoU.flower: 0.3531, IoU.book: 0.4530, IoU.hill: 0.0492, IoU.bench: 0.3783, IoU.countertop: 0.5534, IoU.stove: 0.7233, IoU.palm: 0.5143, IoU.kitchen island: 0.4598, IoU.computer: 0.5493, IoU.swivel chair: 0.4449, IoU.boat: 0.4650, IoU.bar: 0.2543, IoU.arcade machine: 0.2420, IoU.hovel: 0.3897, IoU.bus: 0.7894, IoU.towel: 0.5613, IoU.light: 0.5225, IoU.truck: 0.3246, IoU.tower: 0.3456, IoU.chandelier: 0.6784, IoU.awning: 0.2556, IoU.streetlight: 0.2593, IoU.booth: 0.4210, IoU.television receiver: 0.6715, IoU.airplane: 0.4845, IoU.dirt track: 0.0214, IoU.apparel: 0.2934, IoU.pole: 0.2401, IoU.land: 0.0076, IoU.bannister: 0.0961, IoU.escalator: 0.2124, IoU.ottoman: 0.4543, IoU.bottle: 0.1263, IoU.buffet: 0.3386, IoU.poster: 0.2088, IoU.stage: 0.0833, IoU.van: 0.4151, IoU.ship: 0.6776, IoU.fountain: 0.0035, IoU.conveyer belt: 0.6031, IoU.canopy: 0.1494, IoU.washer: 0.6358, IoU.plaything: 0.2259, IoU.swimming pool: 0.2805, IoU.stool: 0.3989, IoU.barrel: 0.3579, IoU.basket: 0.2231, IoU.waterfall: 0.5980, IoU.tent: 0.9243, IoU.bag: 0.0868, IoU.minibike: 0.5195, IoU.cradle: 0.7615, IoU.oven: 0.2384, IoU.ball: 0.4546, IoU.food: 0.5125, IoU.step: 0.0295, IoU.tank: 0.4380, IoU.trade name: 0.2123, IoU.microwave: 0.3837, IoU.pot: 0.3596, IoU.animal: 0.5238, IoU.bicycle: 0.4572, IoU.lake: 0.6106, IoU.dishwasher: 0.6985, IoU.screen: 0.5906, IoU.blanket: 0.0727, IoU.sculpture: 0.4074, IoU.hood: 0.6196, IoU.sconce: 0.4064, IoU.vase: 0.3232, IoU.traffic light: 0.2620, IoU.tray: 0.0434, IoU.ashcan: 0.4059, IoU.fan: 0.5518, IoU.pier: 0.2112, IoU.crt screen: 0.0509, IoU.plate: 0.3963, IoU.monitor: 0.6334, IoU.bulletin board: 0.3336, IoU.shower: 0.0145, IoU.radiator: 0.4142, IoU.glass: 0.0967, IoU.clock: 0.1861, IoU.flag: 0.3912, Acc.background: nan, Acc.wall: 0.8857, Acc.building: 0.9336, Acc.sky: 0.9711, Acc.floor: 0.9043, Acc.tree: 0.8872, Acc.ceiling: 0.9145, Acc.road: 0.8817, Acc.bed : 0.9545, Acc.windowpane: 0.7604, Acc.grass: 0.8311, Acc.cabinet: 0.7169, Acc.sidewalk: 0.8033, Acc.person: 0.9094, Acc.earth: 0.4631, Acc.door: 0.5961, Acc.table: 0.7655, Acc.mountain: 0.6707, Acc.plant: 0.6423, Acc.curtain: 0.8294, Acc.chair: 0.7036, Acc.car: 0.9048, Acc.water: 0.6183, Acc.painting: 0.8554, Acc.sofa: 0.8204, Acc.shelf: 0.5597, Acc.house: 0.5331, Acc.sea: 0.6907, Acc.mirror: 0.7206, Acc.rug: 0.6128, Acc.field: 0.3426, Acc.armchair: 0.5987, Acc.seat: 0.7555, Acc.fence: 0.4484, Acc.desk: 0.6860, Acc.rock: 0.4604, Acc.wardrobe: 0.6088, Acc.lamp: 0.7541, Acc.bathtub: 0.8115, Acc.railing: 0.4058, Acc.cushion: 0.6481, Acc.base: 0.2822, Acc.box: 0.2742, Acc.column: 0.5392, Acc.signboard: 0.4914, Acc.chest of drawers: 0.5662, Acc.counter: 0.3311, Acc.sand: 0.4817, Acc.sink: 0.7831, Acc.skyscraper: 0.6902, Acc.fireplace: 0.8646, Acc.refrigerator: 0.8114, Acc.grandstand: 0.6218, Acc.path: 0.2113, Acc.stairs: 0.3675, Acc.runway: 0.7980, Acc.case: 0.6497, Acc.pool table: 0.9568, Acc.pillow: 0.6648, Acc.screen door: 0.7829, Acc.stairway: 0.4004, Acc.river: 0.2254, Acc.bridge: 0.6568, Acc.bookcase: 0.4780, Acc.blind: 0.4553, Acc.coffee table: 0.7728, Acc.toilet: 0.9100, Acc.flower: 0.4626, Acc.book: 0.6329, Acc.hill: 0.0666, Acc.bench: 0.4817, Acc.countertop: 0.7071, Acc.stove: 0.8054, Acc.palm: 0.7120, Acc.kitchen island: 0.7437, Acc.computer: 0.6413, Acc.swivel chair: 0.5974, Acc.boat: 0.5661, Acc.bar: 0.2970, Acc.arcade machine: 0.2680, Acc.hovel: 0.4301, Acc.bus: 0.8712, Acc.towel: 0.6620, Acc.light: 0.5763, Acc.truck: 0.4434, Acc.tower: 0.4166, Acc.chandelier: 0.8306, Acc.awning: 0.2844, Acc.streetlight: 0.3166, Acc.booth: 0.4430, Acc.television receiver: 0.7823, Acc.airplane: 0.6288, Acc.dirt track: 0.0533, Acc.apparel: 0.4084, Acc.pole: 0.3589, Acc.land: 0.0106, Acc.bannister: 0.1167, Acc.escalator: 0.2197, Acc.ottoman: 0.5946, Acc.bottle: 0.2003, Acc.buffet: 0.4262, Acc.poster: 0.2657, Acc.stage: 0.0995, Acc.van: 0.5613, Acc.ship: 0.7746, Acc.fountain: 0.0035, Acc.conveyer belt: 0.8525, Acc.canopy: 0.1738, Acc.washer: 0.6533, Acc.plaything: 0.2853, Acc.swimming pool: 0.3390, Acc.stool: 0.5006, Acc.barrel: 0.6488, Acc.basket: 0.3316, Acc.waterfall: 0.7861, Acc.tent: 0.9838, Acc.bag: 0.1030, Acc.minibike: 0.6031, Acc.cradle: 0.9660, Acc.oven: 0.5522, Acc.ball: 0.6694, Acc.food: 0.6011, Acc.step: 0.0339, Acc.tank: 0.4443, Acc.trade name: 0.2467, Acc.microwave: 0.4108, Acc.pot: 0.4191, Acc.animal: 0.5472, Acc.bicycle: 0.7213, Acc.lake: 0.6302, Acc.dishwasher: 0.7357, Acc.screen: 0.6936, Acc.blanket: 0.0852, Acc.sculpture: 0.6552, Acc.hood: 0.6901, Acc.sconce: 0.4731, Acc.vase: 0.4757, Acc.traffic light: 0.3650, Acc.tray: 0.0653, Acc.ashcan: 0.5257, Acc.fan: 0.6827, Acc.pier: 0.2716, Acc.crt screen: 0.0903, Acc.plate: 0.5102, Acc.monitor: 0.7398, Acc.bulletin board: 0.4488, Acc.shower: 0.0203, Acc.radiator: 0.4878, Acc.glass: 0.1080, Acc.clock: 0.2314, Acc.flag: 0.4152